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G-704, Rohan Garima, Behind Shivaji Hsg. Society, Shivaji Nagar, Pune 411 016. 8, Pentagon Harmony, Rt. Bhusari Colony, Pune 411 038. 15, Gajanan Prasad, Near Bhanushali Hall, Nandivli Road, Dombivali (East) A-302, Olympic Society, Sant Dnyashwar Marg, Mulund (East), Mumbai 400 081. Editing! B-403, Niharika Kanakia Spaces, Gladys Alwaras Road, Thane (West) 400 610. Plot No.13, Govind-Keshav, Central Railwaymen#8217;s, Colony, Kalyan Road, Dombivli (East) Giridhar Angan, B/20, Flat No.05, Near Mai Mangeshkar Hospital, Bombay Banglore Highway, Warje, Pune 411 052. For Childhood Essay! B-1, Hrishikesh CHS., 1056/C, Gokhale Road, Shivaji Nagar, Pune 411 016. On-line Donations Received. SHRI VITHALRAO JOSHI CHARITIES TRUST.
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If correlation doesn’t imply causation, then what does? It is editing a commonplace of scientific discussion that correlation does not imply causation. Business Week recently ran an spoof article pointing out some amusing examples of the dangers of good, inferring causation from correlation. For example, the article points out editing that Facebook’s growth has been strongly correlated with the cover letter business development, yield on Greek government bonds: (credit) Despite this strong correlation, it would not be wise to conclude that the success of Facebook has somehow caused the current (2009-2012) Greek debt crisis, nor that the Greek debt crisis has caused the adoption of Facebook!
Of course, while it’s all very well to piously state that correlation doesn’t imply causation, it does leave us with a conundrum: under what conditions, exactly, can we use experimental data to deduce a causal relationship between two or more variables? The standard scientific answer to this question is that (with some caveats) we can infer causality from a well designed randomized controlled experiment. Editing. Unfortunately, while this answer is satisfying in bite you essays, principle and sometimes useful in practice, it’s often impractical or impossible to do a randomized controlled experiment. And so we’re left with the question of whether there are other procedures we can use to editing infer causality from experimental data. And, given that we can find more general procedures for inferring causal relationships, what does causality mean, anyway, for how we reason about a system?
It might seem that the answers to such fundamental questions would have been settled long ago. In fact, they turn out to good dissertations be surprisingly subtle questions. Over the past few decades, a group of scientists have developed a theory of editing, causal inference intended to address these and other related questions. This theory can be thought of bite that feeds and provocations, as an algebra or language for reasoning about cause and editing, effect. Writing Help Dhabi. Many elements of the editing, theory have been laid out in spm speech about school, a famous book by one of the main contributors to the theory, Judea Pearl. Although the theory of causal inference is not yet fully formed, and editing, is still undergoing development, what has already been accomplished is school bully interesting and worth understanding. In this post I will describe one small but important part of the editing, theory of causal inference, a causal calculus developed by Pearl. Thesis Help In Abu. This causal calculus is a set of three simple but powerful algebraic rules which can be used to make inferences about causal relationships.
In particular, I’ll explain how the causal calculus can sometimes (but not always!) be used to infer causation from a set of editing, data, even when a randomized controlled experiment is not possible. Good Introductions. Also in the post, I’ll describe some of the editing, limits of the causal calculus, and university thesis, some of my own speculations and questions. The post is a little technically detailed at points. However, the first three sections of the editing, post are non-technical, and I hope will be of broad interest. Throughout the post I’ve included occasional “Problems for the author”, where I describe problems I’d like to solve, or things I’d like to understand better. Essay Spm Speech About School Bully. Feel free to editing ignore these if you find them distracting, but I hope they’ll give you some sense of what I find interesting about the parents should be blamed obesity, subject. Incidentally, I’m sure many of editing, these problems have already been solved by others; I’m not claiming that these are all open research problems, although perhaps some are. They’re simply things I’d like to understand better. Also in the post I’ve included some exercises for the reader, and some slightly harder problems for the reader.
You may find it informative to work through these exercises and problems. Before diving in, one final caveat: I am not an be blamed obesity, expert on editing, causal inference, nor on cover for international business development, statistics. Editing. The reason I wrote this post was to help me internalize the ideas of the causal calculus. Of Oxford Thesis. Occasionally, one finds a presentation of editing, a technical subject which is beautifully clear and illuminating, a presentation where the author has seen right through the parents should for childhood obesity, subject, and is able to convey that crystalized understanding to others. That’s a great aspirational goal, but I don’t yet have that understanding of causal inference, and these notes don’t meet that standard. Editing. Nonetheless, I hope others will find my notes useful, and of oxford, that experts will speak up to correct any errors or misapprehensions on my part. Let me start by explaining two example problems to editing illustrate some of the difficulties we run into when making inferences about causality. The first is known as Simpson’s paradox. To explain Simpson’s paradox I’ll use a concrete example based on the passage of the Civil Rights Act in the United States in 1964.
In the US House of introductions dissertations, Representatives, 61 percent of editing, Democrats voted for the Civil Rights Act, while a much higher percentage, 80 percent, of Republicans voted for the Act. You might think that we could conclude from this that being Republican, rather than Democrat, was an important factor in thesis dhabi, causing someone to vote for the Civil Rights Act. However, the picture changes if we include an additional factor in the analysis, namely, whether a legislator came from a Northern or Southern state. If we include that extra factor, the situation completely reverses, in both the North and the South. Editing. Here’s how it breaks down: North: Democrat (94 percent), Republican (85 percent)
South: Democrat (7 percent), Republican (0 percent) Yes, you read that right: in both the North and the South, a larger fraction of Democrats than Republicans voted for the Act, despite the fact that overall a larger fraction of Republicans than Democrats voted for for childhood obesity essay the Act. You might wonder how this can possibly be true. I’ll quickly state the editing, raw voting numbers, so you can check that the arithmetic works out, and then I’ll explain why it’s true. You can skip the numbers if you trust my arithmetic. North: Democrat (145/154, 94 percent), Republican (138/162, 85 percent) South: Democrat (7/94, 7 percent), Republican (0/10, 0 percent)
Overall: Democrat (152/248, 61 percent), Republican (138/172, 80 percent) One way of understanding what’s going on is to note that a far greater proportion of Democrat (as opposed to of oxford archive Republican) legislators were from the South. In fact, at the time the House had 94 Democrats, and only 10 Republicans. Because of this enormous difference, the very low fraction (7 percent) of southern Democrats voting for editing the Act dragged down the Democrats’ overall percentage much more than did the even lower fraction (0 percent) of southern Republicans who voted for the Act. (The numbers above are for the House of Congress.
The numbers were different in the Senate, but the same overall phenomenon occurred. I’ve taken the numbers from Wikipedia’s article about Simpson’s paradox, and there are more details there.) If we take a naive causal point of view, this result looks like a paradox. As I said above, the overall voting pattern seems to suggest that being Republican, rather than Democrat, was an important causal factor in voting for the Civil Rights Act. Yet if we look at the individual statistics in both the North and the South, then we’d come to the exact opposite conclusion. To state the same result more abstractly, Simpson’s paradox is the fact that the writing help, correlation between two variables can actually be reversed when additional factors are considered. So two variables which appear correlated can become anticorrelated when another factor is taken into account. You might wonder if results like those we saw in voting on the Civil Rights Act are simply an editing, unusual fluke. Should For Childhood Obesity Essay. But, in editing, fact, this is not that uncommon. Wikipedia’s page on Simpson’s paradox lists many important and university of oxford thesis, similar real-world examples ranging from understanding whether there is editing gender-bias in university admissions to which treatment works best for university of oxford archive kidney stones. Editing. In each case, understanding the causal relationships turns out to be much more complex than one might at first think.
I’ll now go through a second example of Simpson’s paradox, the kidney stone treatment example just mentioned, because it helps drive home just how bad our intuitions about statistics and causality are. Imagine you suffer from school, kidney stones, and your Doctor offers you two choices: treatment A or treatment B. Your Doctor tells you that the two treatments have been tested in a trial, and treatment A was effective for a higher percentage of patients than treatment B. If you’re like most people, at editing this point you’d say “Well, okay, I’ll go with treatment A”. Here’s the gotcha. Keep in mind that this really happened . Suppose you divide patients in the trial up into those with large kidney stones, and those with small kidney stones. Then even though treatment A was effective for a higher overall percentage of patients than treatment B, treatment B was effective for a higher percentage of patients in both groups , i.e., for both large and small kidney stones. So your Doctor could just as honestly have said “Well, you have large [or small] kidney stones, and treatment B worked for a higher percentage of patients with large [or small] kidney stones than treatment A”. If your Doctor had made either one of thesis writing help in abu, these statements, then if you’re like most people you’d have decided to go with treatment B, i.e., the editing, exact opposite treatment.
The kidney stone example relies, of introductions dissertations, course, on the same kind of arithmetic as in the Civil Rights Act voting, and it’s worth stopping to editing figure out for yourself how the claims I made above could possibly be true. If you’re having trouble, you can click through to thesis in abu dhabi the Wikipedia page, which has all the details of the numbers. Now, I’ll confess that before learning about Simpson’s paradox, I would have unhesitatingly done just as I suggested a naive person would. Indeed, even though I’ve now spent quite a bit of time pondering Simpson’s paradox, I’m not entirely sure I wouldn’t still sometimes make the same kind of mistake. I find it more than a little mind-bending that my heuristics about how to behave on the basis of statistical evidence are obviously not just a little wrong, but utterly, horribly wrong. Perhaps I’m alone in having terrible intuition about how to interpret statistics. But frankly I wouldn’t be surprised if most people share my confusion.
I often wonder how many people with real decision-making power – politicians, judges, and so on – are making decisions based on statistical studies, and yet they don’t understand even basic things like Simpson’s paradox. Or, to put it another way, they have not the first clue about statistics. Partial evidence may be worse than no evidence if it leads to editing an illusion of knowledge, and so to overconfidence and good introductions, certainty where none is justified. It’s better to editing know that you don’t know. Correlation, causation, smoking, and lung cancer. As a second example of the about bully, difficulties in editing, establishing causality, consider the relationship between cigarette smoking and lung cancer. In 1964 the United States’ Surgeon General issued a report claiming that cigarette smoking causes lung cancer. Unfortunately, according to thesis writing in abu Pearl the evidence in the report was based primarily on correlations between cigarette smoking and lung cancer.
As a result the report came under attack not just by tobacco companies, but also by some of the world’s most prominent statisticians, including the great Ronald Fisher. They claimed that there could be a hidden factor – maybe some kind of genetic factor – which caused both lung cancer and editing, people to bite the hand and provocations want to smoke (i.e., nicotine craving). If that was true, then while smoking and editing, lung cancer would be correlated, the decision to smoke or not smoke would have no impact on cover for international development, whether you got lung cancer. Now, you might scoff at editing this notion. Dissertations. But derision isn’t a principled argument. And, as the editing, example of good dissertations, Simpson’s paradox showed, determining causality on the basis of correlations is tricky, at best, and can potentially lead to contradictory conclusions.
It’d be much better to have a principled way of using data to conclude that the relationship between smoking and lung cancer is editing not just a correlation, but rather that there truly is a causal relationship. One way of demonstrating this kind of causal connection is to do a randomized, controlled experiment. We suppose there is some experimenter who has the power to intervene with a person, literally forcing them to thesis help in abu either smoke (or not) according to the whim of the experimenter. The experimenter takes a large group of people, and randomly divides them into two halves. One half are forced to editing smoke, while the other half are forced not to smoke. By doing this the experimenter can break the relationship between smoking and any hidden factor causing both smoking and lung cancer. By comparing the cancer rates in the group who were forced to smoke to those who were forced not to smoke, it would then be possible determine whether or not there is truly a causal connection between smoking and lung cancer.
This kind of introductions, randomized, controlled experiment is highly desirable when it can be done, but experimenters often don’t have this power. In the case of smoking, this kind of experiment would probably be illegal today, and, I suspect, even decades into editing, the past. And even when it’s legal, in letter for international development, many cases it would be impractical, as in the case of the Civil Rights Act, and for editing many other important political, legal, medical, and econonomic questions. To help address problems like the two example problems just discussed, Pearl introduced a causal calculus. Essay Bully. In the remainder of this post, I will explain the rules of the causal calculus, and use them to analyse the smoking-cancer connection. We’ll see that even without doing a randomized controlled experiment it’s possible (with the aid of some reasonable assumptions) to infer what the outcome of a randomized controlled experiment would have been, using only relatively easily accessible experimental data, data that doesn’t require experimental intervention to force people to smoke or not, but which can be obtained from purely observational studies. To state the rules of the editing, causal calculus, we’ll need several background ideas. I’ll explain those ideas over parents should for childhood obesity the next three sections of this post. Editing. The ideas are causal models (covered in this section), causal conditional probabilities , and d-separation , respectively.
It’s a lot to swallow, but the ideas are powerful, and worth taking the time to understand. With these notions under our belts, we’ll able to understand the rules of the causal calculus. To understand causal models, consider the following graph of possible causal relationships between smoking, lung cancer, and university of oxford thesis archive, some unknown hidden factor (say, a hidden genetic factor): This is a quite general model of causal relationships, in the sense that it includes both the suggestion of the US Surgeon General (smoking causes cancer) and also the suggestion of the tobacco companies (a hidden factor causes both smoking and cancer). Indeed, it also allows a third possibility: that perhaps both smoking and some hidden factor contribute to lung cancer. Editing. This combined relationship could potentially be quite complex: it could be, for example, that smoking alone actually reduces the chance of lung cancer, but the feeds you essays and provocations, hidden factor increases the chance of editing, lung cancer so much that someone who smokes would, on obesity, average, see an editing, increased probability of lung cancer. This sounds unlikely, but later we’ll see some toy model data which has exactly this property. Of course, the cover, model depicted in the graph above is editing not the most general possible model of causal relationships in this system; it’s easy to good imagine much more complex causal models.
But at the very least this is an interesting causal model, since it encompasses both the US Surgeon General and the tobacco company suggestions. I’ll return later to the possibility of more general causal models, but for now we’ll simply keep this model in mind as a concrete example of a causal model. Mathematically speaking, what do the editing, arrows of causality in the diagram above mean? We’ll develop an answer to that question over essay spm speech about bully the next few paragraphs. It helps to editing start by moving away from the specific smoking-cancer model to allow a causal model to university be based on a more general graph indicating possible causal relationships between a number of variables: Each vertex in this causal model has an associated random variable, . For example, in editing, the causal model above could be a two-outcome random variable indicating the university archive, presence or absence of some gene that exerts an influence on whether someone smokes or gets lung cancer, indicates “smokes” or “does not smoke”, and indicates “gets lung cancer” or “doesn’t get lung cancer”. Editing. The other variables and would refer to other potential dependencies in this (somewhat more complex) model of the smoking-cancer connection.
A notational convention that we’ll use often is to interchangeably use to refer to a random variable in the causal model, and also as a way of labelling the corresponding vertex in the graph for the causal model. Thesis Help In Abu Dhabi. It should be clear from context which is meant. We’ll also sometimes refer interchangeably to the causal model or to the associated graph. For the notion of causality to make sense we need to constrain the editing, class of graphs that can be used in a causal model. Obviously, it’d make no sense to have loops in the graph: We can’t have causing causing causing ! At least, not without a time machine. Because of this we constrain the graph to be a directed acyclic graph, meaning a (directed) graph which has no loops in it. By the way, I must admit that I’m not a fan of the term directed acyclic graph. It sounds like a very complicated notion, at least to my ear, when what it means is very simple: a graph with no loops. I’d really prefer to call it a “loop-free graph”, or something like that.
Unfortunately, the essay about school, “directed acyclic graph” nomenclature is editing pretty standard, so we’ll go with it. Our picture so far is that a causal model consists of a directed acyclic graph, whose vertices are labelled by good random variables . To complete our definition of causal models we need to capture the allowed relationships between those random variables. Intuitively, what causality means is that for any particular the only random variables which directly influence the editing, value of thesis writing, are the parents of , i.e., the collection of random variables which are connected directly to . For instance, in the graph shown below (which is the editing, same as the complex graph we saw a little earlier), we have : Now, of course, vertices further back in dissertations, the graph – say, the parents of the parents – could, of course, influence the value of . But it would be indirect, an influence mediated through the parent vertices. Note, by the way, that I’ve overloaded the notation, using to denote a collection of random variables. I’ll use this kind of overloading quite a bit in the rest of this post. In particular, I’ll often use the notation (or , or ) to denote a subset of random variables from the graph. Motivated by editing the above discussion, one way we could define causal influence would be to require that be a function of its parents: where is some function. In fact, we’ll allow a slightly more general notion of causal influence, allowing to essay spm speech about school bully not just be a deterministic function of the editing, parents, but a random function. We do this by requiring that be expressible in the form: where is a function, and is a collection of random variables such that: (a) the are independent of one another for different values of ; and spm speech about school bully, (b) for editing each , is independent of all variables , except when is itself, or a descendant of . The intuition is that the are a collection of auxiliary random variables which inject some extra randomness into university of oxford archive, (and, through , its descendants), but which are otherwise independent of the variables in editing, the causal model. Summing up, a causal model consists of a directed acyclic graph, , whose vertices are labelled by random variables, , and each is expressible in should be blamed for childhood obesity essay, the form for some function . The are independent of one another, and each is independent of all variables , except when is or a descendant of . In practice, we will not work directly with the editing, functions or the auxiliary random variables . Business Development. Instead, we’ll work with the following equation, which specifies the causal model’s joint probability distribution as a product of conditional probabilities:
I won’t prove this equation, but the expression should be plausible, and is pretty easy to editing prove; I’ve asked you to prove it as an optional exercise below. Prove the introductions, above equation for the joint probability distribution. (Simpson’s paradox in causal models) Consider the causal model of smoking introduced above. Editing. Suppose that the hidden factor is a gene which is either switched on or off. If on, it tends to make people both smoke and get lung cancer.
Find explicit values for conditional probabilities in the causal model such that , and that you essays and provocations, yet if the additional genetic factor is taken into account this relationship is reversed. Editing. That is, we have both and . An alternate, equivalent approach to defining causal models is as follows: (1) all root vertices (i.e., vertices with no parents) in for childhood essay, the graph are labelled by independent random variables. (2) augment the editing, graph by introducing new vertices corresponding to the . Help Dhabi. These new vertices have single outgoing edges, pointing to . Editing. (3) Require that non-root vertices in the augmented graph be deterministic functions of their parents. Dissertations. The disadvantage of editing, this definition is that it introduces the overhead of university thesis archive, dealing with the augmented graph. But the definition also has the advantage of cleanly separating the stochastic and deterministic components, and editing, I wouldn’t be surprised if developing the cover for international business development, theory of editing, causal inference from this point of view was stimulating, at the very least, and may possibly have some advantages compared to the standard approach. So the problem I set myself (and anyone else who is interested!) is to carry the consequences of help dhabi, this change through the rest of the theory of causal inference, looking for advantages and disadvantages. I’ve been using terms like “causal influence” somewhat indiscriminately in the discussion above, and so I’d like to pause to editing discuss a bit more carefully about what is meant here, and what nomenclature we should use going forward. All the arrows in bite the hand you essays and provocations, a causal model indicate are the possibility of editing, a direct causal influence. This results in two caveats on about school bully, how we think about editing, causality in these models. First, it may be that a child random variable is actually completely independent of the value of one (or more) of good introductions, its parent random variables.
This is, admittedly, a rather special case, but is editing perfectly consistent with the definition. For example, in a causal model like. it is possible that the outcome of cancer might be independent of the hidden causal factor or, for that matter, that it might be independent of whether someone smokes or not. (Indeed, logically, at least, it may be independent of letter business, both, although of course that’s not what we’ll find in the real world.) The second caveat in how we think about the arrows and causality is that the arrows only editing capture the direct causal influences in the model. It is thesis writing possible that in a causal model like. will have a causal influence on through its influence on and . This would be an editing, indirect causal influence, mediated by other random variables, but it would still be a causal influence. Spm Speech About Bully. In the editing, next section I’ll give a more formal definition of causal influence that can be used to make these ideas precise. In this section I’ll explain what I think is the most imaginative leap underlying the causal calculus. Good Dissertations. It’s the introduction of the concept of editing, causal conditional probabilities . The notion of ordinary conditional probabilities is no doubt familiar to you. It’s pretty straightforward to university of oxford thesis do experiments to editing estimate conditional probabilities such as , simply by looking at the population of spm speech about bully, people who smoke, and editing, figuring out what fraction of those people develop cancer. University Archive. Unfortunately, for the purpose of editing, understanding the causal relationship between smoking and cancer, isn’t the quantity we want. As the tobacco companies pointed out, there might well be a hidden genetic factor that makes it very likely that you’ll see cancer in anyone who smokes, but that wouldn’t therefore mean that smoking causes cancer.
As we discussed earlier, what you’d really like to do in this circumstance is a randomized controlled experiment in thesis help, which it’s possible for the experimenter to editing force someone to smoke (or not smoke), breaking the causal connection between the hidden factor and smoking. In such an experiment you really could see if there was a causal influence by looking at what fraction of people who smoked got cancer. In particular, if that fraction was higher than in the overall population then you’d be justified in concluding that smoking helped cause cancer. In practice, it’s probably not practical to do this kind of randomized controlled experiment. But Pearl had what turns out to be a very clever idea: to be blamed imagine a hypothetical world in which it really is possible to force someone to (for example) smoke, or not smoke. In particular, he introduced a conditional causal probability , which is the conditional probability of editing, cancer in this hypothetical world. This should be read as the (causal conditional) probability of cancer given that we “do” smoking, i.e., someone has been forced to smoke in a (hypothetical) randomized experiment. Now, at first sight this appears a rather useless thing to do. But what makes it a clever imaginative leap is that although it may be impossible or impractical to do a controlled experiment to determine , Pearl was able to establish a set of rules – a causal calculus – that such causal conditional probabilities should obey.
And, by making use of this causal calculus, it turns out to bite the hand that you essays and provocations sometimes be possible to infer the value of probabilities such as , even when a controlled, randomized experiment is impossible. Editing. And that’s a very remarkable thing to be able to archive do, and why I say it was so clever to have introduced the notion of causal conditional probabilities. We’ll discuss the rules of the causal calculus later in this post. For now, though, let’s develop the notion of causal conditional probabilities. Suppose we have a causal model of some phenomenon: Now suppose we introduce an external experimenter who is able to intervene to editing deliberately set the value of cover letter for international development, a particular variable to . In other words, the experimenter can override the other causal influences on that variable. This is editing equivalent to having a new causal model: In this new causal model, we’ve represented the experimenter by a new vertex, which has as a child the vertex . All other parents of of oxford thesis, are cut off, i.e., the edges from the parents to are deleted from the editing, graph. In this case that means the edge from to has been deleted. This represents the fact that the experimenter’s intervention overrides the other causal influences. (Note that the edges to the children of essay spm speech bully, are left undisturbed.) In fact, it’s even simpler (and equivalent) to consider a causal model where the parents have been cut off from , and no extra vertex added: This model has no vertex explicitly representing the experimenter, but rather the relation is replaced by the relation . We will denote this graph by editing , indicating the graph in which all edges pointing to writing have been deleted.
We will call this a perturbed graph , and the corresponding causal model a perturbed causal model . In the editing, perturbed causal model the only change is to delete the edges to , and to replace the relation by about bully the relation . Our aim is to use this perturbed causal model to compute the editing, conditional causal probability . In this expression, indicates that the term is cover omitted before the , since the value of is editing set on the right. By definition, the causal conditional probability is just the value of the probability distribution in the perturbed causal model, . School Bully. To compute the value of the probability in the perturbed causal model, note that the probability distribution in the original causal model was given by. where the editing, product on the right is over good introductions all vertices in the causal model. Editing. This expression remains true for the perturbed causal model, but a single term on the right-hand side changes: the dissertations, conditional probability for the term. In particular, this term gets changed from to , since we have fixed the value of to editing be . As a result we have: This equation is a fundamental expression, capturing what it means for an experimenter to intervene to set the value of some particular variable in a causal model. It can easily be generalized to a situation where we partition the variables into two sets, and , where are the of oxford thesis, variables we suppose have been set by intervention in a (possibly hypothetical) randomized controlled experiment, and editing, are the remaining variables:
Note that on the right-hand side the values for are assumed to be given by essay spm speech about the appropriate values from and . Editing. The expression  can be viewed as a definition of spm speech about bully, causal conditional probabilities. But although this expression is editing fundamental to understanding the causal calculus, it is not always useful in practice. The problem is of oxford thesis that the values of some of the variables on the right-hand side may not be known, and editing, cannot be determined by experiment. Consider, for example, the case of smoking and cancer. Recall our causal model: What we’d like is to compute . Unfortunately, we immediately run into a problem if we try to use the expression on the right of equation : we’ve got no way of estimating the conditional probabilities for smoking given the hidden common factor. So we can’t obviously compute . And, as you can perhaps imagine, this is the kind of problem that will come up a lot whenever we’re worried about the possible influence of some hidden factor. All is university of oxford archive not lost, however. Editing. Just because we can’t compute the expression on the right of  directly doesn’t mean we can’t compute causal conditional probabilities in other ways, and we’ll see below how the causal calculus can help solve this kind of problem. It’s not a complete solution – we shall see that it doesn’t always make it possible to compute causal conditional probabilities. But it does help.
In particular, we’ll see that although it’s not possible to compute for this causal model, it is possible to compute in a very similar causal model, one that still has a hidden factor. With causal conditional probabilities defined, we’re now in feeds, position to define more precisely what we mean by causal influence. Suppose we have a causal model, and and are distinct random variables (or disjoint subsets of random variables). Editing. Then we say has a causal influence over if there are values and of and of such that . Of Oxford Thesis Archive. In other words, an external experimenter who can intervene to change the value of can cause a corresponding change in the distribution of values at . The following exercise gives an information-theoretic justification for this definition of causal influence: it shows that an experimenter who can intervene to editing set can transmit information to if and only if the above condition for causal inference is met. (The causal capacity) This exercise is for people with some background in information theory.
Suppose we define the causal capacity between and to bite feeds and provocations be , where is the editing, mutual information, the maximization is over possible distributions for school (we use the hat to editing indicate that the value of is being set by intervention), and is the corresponding random variable at , with distribution . Shannon’s noisy channel coding theorem tells us that an parents should for childhood essay, external experimenter who can intervene to set the value of can transmit information to an observer at editing at a maximal rate set by the causal capacity. Show that the causal capacity is greater than zero if and only if has a causal influence over . We’ve just defined a notion of causal influence between two random variables in essay about school, a causal model. What about when we say something like “Event A” causes “Event B”? What does this mean? Returning to editing the smoking-cancer example, it seems that we would say that smoking causes cancer provided , so that if someone makes the choice to smoke, uninfluenced by cover for international business other causal factors, then they would increase their chance of cancer. Intuitively, it seems to me that this notion of events causing one another should be related to the notion of editing, causal influence just defined above. But I don’t yet see quite how to do that. The first problem below suggests a conjecture in this direction: Suppose and are random variables in a causal model such that for some pair of values and . Does this imply that exerts a causal influence on university of oxford archive, ? (Sum-over-paths for causal conditional probabilities?) I believe a kind of sum-over-paths formulation of editing, causal conditional probabilities is good possible, but haven’t worked out details.
The idea is editing as follows (the details may be quite wrong, but I believe something along these lines should work). Supose and are single vertices (with corresponding random variables) in a causal model. Then I would like to show first that if is not an ancestor of then , i.e., intervention does nothing. Second, if is an ancestor of then may be obtained by letter development summing over all directed paths from to in , and computing for each path a contribution to the sum which is editing a product of conditional probabilities along the essay, path. (Note that we may need to consider the same path multiple times in the sum, since the editing, random variables along the path may take different values). We used causal models in our definition of causal conditional probabilities. Good. But our informal definiton – imagine a hypothetical world in which it’s possible to force a variable to take a particular value – didn’t obviously require the use of a causal model. Indeed, in a real-world randomized controlled experiment it may be that there is no underlying causal model. This leads me to wonder if there is editing some other way of cover for international development, formalizing the informal definition we’ve given? Another way of framing the last problem is that I’m concerned about the empirical basis for causal models. How should we go about constructing such models?
Are they fundamental, representing true facts about the editing, world, or are they modelling conveniences? (This is by essay school no means a dichotomy.) It would be useful to editing work through many more examples, considering carefully the origin of the functions and of the auxiliary random variables . In this section we’ll develop a criterion that Pearl calls directional separation ( d-separation , for short). Good Introductions. What d-separation does is editing let us inspect the graph of a causal model and conclude that a random variable in the model can’t tell us anything about the of oxford thesis archive, value of another random variable in editing, the model, or vice versa. To understand d-separation we’ll start with a simple case, and then work through increasingly complex cases, building up our intuition. I’ll conclude by giving a precise definition of d-separation, and by explaining how d-separation relates to university the concept of conditional independence of random variables. Here’s the first simple causal model: Clearly, knowing can in editing, general tell us something about in this kind of causal model, and so in this case and are not d-separated. We’ll use the term d-connected as a synonym for “not d-separated”, and essay about school bully, so in this causal model and are d-connected. By contrast, in editing, the following causal model and don’t give us any information about each other, and so they are d-separated: A useful piece of letter business, terminology is to editing say that a vertex like the middle vertex in this model is a collider for the path from to , meaning a vertex at which both edges along the path are incoming.
What about the causal model: In this case, it is possible that knowing will tell us something about , because of their common ancestry. It’s like the way knowing the genome for one sibling can give us information about the genome of another sibling, since similarities between the genomes can be inferred from the common ancestry. We’ll call a vertex like the middle vertex in this model a fork for the path from to , meaning a vertex at which both edges are outgoing. Construct an explicit causal model demonstrating the assertion of the last paragraph. For example, you may construct a causal model in which and are joined by writing in abu a fork, and where is actually a function of . Suppose we have a path from to in a causal model. Let be the number of colliders along the path, and let be the number of forks along the path.
Show that can only take the values or , i.e., the number of forks and editing, colliders is either the same or differs by at most one. We’ll say that a path (of any length) from to that contains a collider is a blocked path. By contrast, a path that contains no colliders is called an unblocked path. Bite The Hand That You Essays. (Note that by the above exercise, an unblocked path must contain either one or no forks.) In general, we define and to be d-connected if there is an unblocked path between them. We define them to be d-separated if there is no such unblocked path. It’s worth noting that the concepts of d-separation and d-connectedness depend only on editing, the graph topology and on which vertices and have been chosen.
In particular, they don’t depend on the nature of the random variables and , merely on the identity of the corresponding vertices. Cover Business. As a result, you can determine d-separation or d-connectdness simply by inspecting the graph. This fact – that d-separation and d-connectdness are determined by the graph – also holds for the more sophisticated notions of editing, d-separation and the hand feeds and provocations, d-connectedness we develop below. With that said, it probably won’t surprise you to learn that the concept of d-separation is closely related to whether or not the random variables and are independent of one another. This is a connection you can (optionally) develop through the following exercises. Editing. I’ll state a much more general connection below. Suppose that and are d-separated. Show that and thesis, are independent random variables, i.e., that . Editing. Suppose we have two vertices which are d-connected in a graph . Explain how to should be blamed construct a causal model on editing, that graph such that the parents be blamed, random variables and corresponding to editing those two vertices are not independent. The last two exercises almost but don’t quite claim that random variables and in a causal model are independent if and only if they are d-separated.
Why does this statement fail to be true? How can you modify the statement to make it true? So far, this is pretty simple stuff. It gets more complicated, however, when we extend the bite the hand feeds and provocations, notion of d-separation to cases where we are conditioning on editing, already knowing the value of one or more random variables in the causal model. Consider, for example, the graph: Now, if we know , then knowing doesn’t give us any additional information about , since by our original definition of a causal model is already a function of and some auxiliary random variables which are independent of . So it makes sense to say that blocks this path from to , even though in the unconditioned case this path would not have been considered blocked. We’ll also say that and are d-separated, given . It is parents should be blamed for childhood essay helpful to give a name to vertices like the middle vertex in Figure A, i.e., to vertices with one ingoing and one outgoing edge.
We’ll call such vertices a traverse along the path from to . Using this language, the lesson of the above discussion is editing that if is in a traverse along a path from to should be blamed , then the path is blocked. By contrast, consider this model: In this case, knowing will in general give us additional information about , even if we know . This is because while blocks one path from to there is another unblocked path from to editing . And so we say that and are d-connected, given . Another case similar to Figure A is the model with a fork: Again, if we know , then knowing as well doesn’t give us any extra information about (or vice versa). Introductions. So we’ll say that in this case is blocking the path from to , even though in the unconditioned case this path would not have been considered blocked. Again, in this example and are d-separated, given . The lesson of this model is that if is located at a fork along a path from to , then the editing, path is blocked. A subtlety arises when we consider a collider: In the unconditioned case this would have been considered a blocked path. And, naively, it seems as though this should still be the case: at first sight (at least according to introductions my intuition) it doesn’t seem very likely that can give us any additional information about (or vice versa), even given that is known. Yet we should be cautious, because the argument we made for editing the graph in Figure A breaks down: we can’t say, as we did for Figure A, that is a function of and some auxiliary independent random variables. In fact, we’re wise to be cautious because and really can tell us something extra about one another, given a knowledge of . This is a phenomenon which Pearl calls Berkson’s paradox . He gives the example of a graduate school in music which will admit a student (a possibility encoded in the value of ) if either they have high undergraduate grades (encoded in cover for international, ) or some other evidence that they are exceptionally gifted at music (encoded in ). It would not be surprising if these two attributes were anticorrelated amongst students in the program, e.g., students who were admitted on the basis of exceptional gifts would be more likely than otherwise to have low grades.
And so in this case knowledge of (exceptional gifts) would give us knowledge of (likely to have low grades), conditioned on knowledge of editing, (they were accepted into the program). Another way of seeing Berkson’s paradox is to construct an explicit causal model for the graph in Figure B. Thesis Writing In Abu Dhabi. Consider, for editing example, a causal model in which and are independent random bits, or , chosen with equal probabilities . We suppose that , where is addition modulo . This causal model does, indeed, have the structure of Figure B. But given that we know the value , knowing the good introductions, value of editing, tells us everything about , since . As a result of this discussion, in the causal graph of Figure B we’ll say that unblocks the bite the hand that feeds you essays and provocations, path from to , even though in the unconditioned case the path would have been considered blocked. And we’ll also say that in editing, this causal graph and are d-connected, conditional on parents should obesity, . The immediate lesson from the graph of Figure B is that and can tell us something about editing, one another, given , if there is a path between and where the only collider is at . In fact, the same phenomenon can occur even in dissertations, this graph: To see this, suppose we choose and as in the example just described above, i.e., independent random bits, or , chosen with equal probabilities . Editing. We will let the unlabelled vertex be . And, finally, we choose . Then we see as before that can tell us something about cover, , given that we know , because . The general intuition about graphs like that in Figure C is that knowing allows us to editing infer something about the ancestors of , and so we must act as though those ancestors are known, too. As a result, in this case we say that unblocks the cover letter for international business, path from to , since has an ancestor which is a collider on the path from to . And so in this case is d-connected to editing , given . Given the writing dhabi, discussion of Figure C that we’ve just had, you might wonder why forks or traverses which are ancestors of editing, can’t block a path, for similar reasons? For instance, why don’t we consider and to be d-separated, given , in the following graph: The reason, of course, is that it’s easy to construct examples where tells us something about in addition to what we already know from . And so we can’t consider and to be d-separated, given , in this example.
These examples motivate the following definition: Definition: Let , and essay spm speech, be disjoint subsets of editing, vertices in a causal model. Consider a path from a vertex in to essay spm speech about school bully a vertex in . We say the path is editing blocked by if the path contains either: (a) a collider which is not an ancestor of , or (b) a fork which is in , or (c) a traverse which is in parents should be blamed for childhood obesity, . We say the editing, path is unblocked if it is not blocked. We say that and are d-connected , given , if there is an good introductions, unblocked path between some vertex in and some vertex in editing, . and are d-separated , given , if they are not d-connected. Saying “ and are d-separated given ” is a bit of a mouthful, and so it’s helpful to have an abbreviated notation. We’ll use the abbreviation . Note that this notation includes the graph ; we’ll sometimes omit the essay spm speech school bully, graph when the context is editing clear. We’ll write to denote unconditional d-separation. As an aside, Pearl uses a similar but slightly different notation for d-separation, namely . Unfortunately, while the symbol looks like a LaTeX symbol, it’s not, but is most easily produced using a rather dodgy LaTeX hack.
Instead of development, using that hack over editing and over cover for international business development again, I’ve adopted a more standard LaTeX notation. While I’m making asides, let me make a second: when I was first learning this material, I found the “d” for “directional” in d-separation and d-connected rather confusing. It suggested to me that the key thing was having a directed path from one vertex to editing the other, and that the complexities of colliders, forks, and so on were a sideshow. Of course, they’re not, they’re central to the whole discussion. For this reason, when I was writing these notes I considered changing the terminology to i-separated and i-connected, for informationally-separated and feeds you essays, informationally-connected. Ultimately I decided not to do this, but I thought mentioning the issue might be helpful, in part to editing reassure readers (like me) who thought the “d” seemed a little mysterious. Okay, that’s enough asides, let’s get back to the main track of discussion. We saw earlier that (unconditional) d-separation is closely connected to the independence of random variables.
It probably won’t surprise you to learn that conditional d-separation is closely connected to conditional independence of random variables. Recall that two sets of random variables and are conditionally independent , given a third set of random variables , if . The following theorem shows that d-separation gives a criterion for archive when conditional independence occurs in a causal model: Theorem (graphical criterion for conditional independence): Let be a graph, and let , and be disjoint subsets of vertices in that graph. Then and are d-separated, given , if and only if for all causal models on the random variables corresponding to editing and are conditionally independent, given . (Update: Thanks to Rob Spekkens for pointing out an good dissertations, error in my original statement of this theorem.) I won’t prove the theorem here.
However, it’s not especially difficult if you’ve followed the discussion above, and is a good problem to editing work through: The concept of d-separation plays a central role in the causal calculus. My sense is that it should be possible to find a cleaner and more intuitive definition that substantially simplifies many proofs. It’d be good to spend some time trying to find such a definition. We’ve now got all the concepts we need to state the rules of the causal calculus. There are three rules. The rules look complicated at first, although they’re easy to use once you get familiar with them.
For this reason I’ll start by explaining the university of oxford archive, intuition behind the first rule, and how you should think about that rule. Having understood how to editing think about the first rule it’s easy to get the hang of all three rules, and so after that I’ll just outright state all three rules. In what follows, we have a causal model on a graph , and are disjoint subsets of the variables in be blamed, the causal model. Recall also that denotes the perturbed graph in which all edges pointing to from the parents of have been deleted. This is the graph which results when an experimenter intervenes to set the editing, value of , overriding other causal influences on . Rule 1: When can we ignore observations: I’ll begin by stating the first rule in all its glory, but don’t worry if you don’t immediately grok the whole rule. Instead, just take a look, and try to start getting your head around it. What we’ll do then is good dissertations look at some simple special cases, which are easily understood, and gradually build up to an understanding of what the editing, full rule is parents should for childhood essay saying. Okay, so here’s the first rule of the causal calculus. What it tells us is that when , then we can ignore the observation of in computing the probability of , conditional on editing, both and an intervention to set : To understand why this rule is true, and what it means, let’s start with a much simpler case. Let’s look at what happens to the rule when there are no or variables in the mix.
In this case, our starting assumption simply becomes that is d-separated from in the original (unperturbed) graph . There’s no need to writing worry about because there’s no variable whose value is being set by intervention. In this circumstance we have , so is independent of . But the statement of the rule in this case is merely that , which is, indeed, equivalent to the standard definition of and being independent. In other words, the first rule is editing simply a generalization of what it means for and to be independent. The full rule generalizes the notion of independence in essay spm speech, two ways: (1) by adding in an extra variable whose value has been determined by passive observation; and (2) by adding in an extra variable whose value has been set by intervention. We’ll consider these two ways of generalizing separately in editing, the next two paragraphs. We begin with generalization (1), i.e., there is for international no variable in the mix. In this case, our starting assumption becomes that is d-separated from , given , in the graph . By the graphical criterion for conditional independence discussed in the last section this means that is conditionally independent of , given , and so , which is exactly the statement of the rule.
And so the first rule can be viewed as a generalization of editing, what it means for and to be independent, conditional on . Now let’s look at the other generalization, (2), in which we’ve added an extra variable whose value has been set by should be blamed obesity essay intervention, and where there is no variable in the mix. In this case, our starting assumption becomes that is d-separated from , given , in editing, the perturbed graph . In this case, the graphical criterion for conditional indepenence tells us that is independent from , conditional on the value of being set by experimental intervention, and so . Again, this is exactly the statement of the rule. The full rule, of introductions dissertations, course, merely combines both these generalizations in the obvious way. It is really just an explicit statement of the content of the graphical criterion for editing conditional independence, in good introductions, a context where has been observed, and the value of set by experimental intervention. The rules of the causal calculus: All three rules of the causal calculus follow a similar template to the first rule: they provide ways of using facts about the editing, causal structure (notably, d-separation) to make inferences about conditional causal probabilities. I’ll now state all three rules.
The intuition behind rules 2 and 3 won’t necessarily be entirely obvious, but after our discussion of rule 1 the remaining rules should at least appear plausible and comprehensible. Should Be Blamed For Childhood Obesity. I’ll have bit more to say about intuition below. As above, we have a causal model on a graph , and are disjoint subsets of the variables in the causal model. denotes the editing, perturbed graph in which all edges pointing to from the parents of have been deleted. For International Business. denotes the graph in which all edges pointing out from to the children of have been deleted. We will also freely use notations like to editing denote combinations of cover letter for international business, these operations. Rule 1: When can we ignore observations: Suppose . Then: Rule 2: When can we ignore the act of intervention: Suppose . Then: Rule 3: When can we ignore an intervention variable entirely: Let denote the set of nodes in which are not ancestors of . Suppose . Editing. Then: In a sense, all three rules are statements of conditional independence.
The first rule tells us when we can ignore an observation. The second rule tells us when we can ignore the act of intervention (although that doesn’t necessarily mean we can ignore the bite that feeds and provocations, value of the variable being intervened with). Editing. And the third rule tells us when we can ignore an intervention entirely, both the of oxford thesis, act of intervention, and the value of the variable being intervened with. I won’t prove rule 2 or rule 3 – this post is already quite long enough. Editing. (If I ever significantly revise the post I may include the proofs). The important thing to take away from these rules is that they give us conditions on business development, the structure of causal models so that we know when we can ignore observations, acts of intervention, or even entire variables that have been intervened with. This is editing obviously a powerful set of tools to be working with in manipulating conditional causal probabilities! Indeed, according to bite the hand that feeds and provocations Pearl there’s even a sense in which this set of rules is complete , meaning that using these rules you can identify all causal effects in a causal model. I haven’t yet understood the proof of this result, or even exactly what it means, but thought I’d mention it. The proof is in editing, papers by Shpitser and Pearl and Huang and Valtorta. If you’d like to see the proofs of the rules of the calculus, you can either have a go at proving them yourself, or you can read the proof. Suppose the conditions of rules 1 and cover, 2 hold.
Can we deduce that the editing, conditions of rule 3 also hold? Using the causal calculus to analyse the smoking-lung cancer connection. We’ll now use the causal calculus to analyse the connection between smoking and lung cancer. Earlier, I introduced a simple causal model of this connection: The great benefit of this model was that it included as special cases both the spm speech about school, hypothesis that smoking causes cancer and the hypothesis that some hidden causal factor was responsible for both smoking and editing, cancer. It turns out, unfortunately, that the causal calculus doesn’t help us analyse this model. I’ll explain why that’s the case below. Of Oxford Thesis. However, rather than worrying about editing, this, at this stage it’s more instructive to work through an example showing how the causal calculus can be helpful in analysing a similar but slightly modified causal model. Thesis Writing In Abu Dhabi. So although this modification looks a little mysterious at first, for editing now I hope you’ll be willing to dissertations accept it as given. The way I’m going to editing modify the causal model is by introducing an extra variable, namely, whether someone has appreciable amounts of tar in their lungs or not: (By tar, I don’t mean “tar” literally, but rather all the the hand feeds you essays, material deposits found as a result of smoking.)
This causal model is a plausible modification of the original causal model. Editing. It is at bite the hand that least plausible to suppose that smoking causes tar in the lungs and that those deposits in turn cause cancer. But if the hidden causal factor is genetic, as the tobacco companies argued was the case, then it seems highly unlikely that the genetic factor caused tar in the lungs, except by the indirect route of editing, causing those people to smoke. (I’ll come back to what happens if you refuse to accept this line of reasoning. Thesis. For now, just go with it.) Our goal in this modified causal model is to compute probabilities like . Editing. What we’ll show is that the causal calculus lets us compute this probability entirely in terms of probabilities like and other probabilities that don’t involve an intervention, i.e., that don’t involve . This means that we can determine without needing to know anything about the hidden factor. We won’t even need to know the nature of the hidden factor. Writing Dhabi. It also means that we can determine without needing to intervene to force someone to smoke or not smoke, i.e., to set the value for editing . In other words, the causal calculus lets us do something that seems almost miraculous: we can figure out the probability that someone would get cancer given that they are in the smoking group in a randomized controlled experiment, without needing to cover for international do the randomized controlled experiment. Editing. And this is true even though there may be a hidden causal factor underlying both smoking and cancer. Okay, so how do we compute ? The obvious first question to of oxford thesis ask is whether we can apply rule 2 or rule 3 directly to editing the conditional causal probability . If rule 2 applies, for example, it would say that intervention doesn’t matter, and so . Intuitively, this seems unlikely. We’d expect that intervention really can change the probability of cancer given smoking, because intervention would override the cover letter for international, hidden causal factor.
If rule 3 applies, it would say that , i.e., that an intervention to force someone to smoke has no impact on whether they get cancer. Editing. This seems even more unlikely than rule 2 applying. However, as practice and a warm up, let’s work through the details of seeing whether rule 2 or rule 3 can be applied directly to . For rule 2 to apply we need . To check whether this is true, recall that is the graph with the edges pointing out from deleted: Obviously, is not d-separated from in this graph, since and have a common ancestor. This reflects the fact that the hidden causal factor indeed does influence both and . So we can’t apply rule 2.
What about rule 3? For this to apply we’d need . Recall that is the graph with the edges pointing toward deleted: Again, is not d-separated from , in this case because we have an university of oxford, unblocked path directly from to . This reflects our intuition that the value of editing, can influence , even when the value of parents for childhood essay, has been set by intervention. So we can’t apply rule 3. Okay, so we can’t apply the editing, rules of the causal calculus directly to determine . Is there some indirect way we can determine this probability? An experienced probabilist would at this point instinctively wonder whether it would help to condition on the value of parents obesity essay, , writing: Of course, saying an experienced probabilist would instinctively do this isn’t quite the same as explaining why one should do this! However, it is at least a moderately obvious thing to do: the editing, only extra information we potentially have in the problem is , and so it’s certainly somewhat natural to try to introduce that variable into thesis writing in abu dhabi, the problem. Editing. As we shall see, this turns out to be a wise thing to parents should for childhood obesity essay do.
I used without proof the equation . This should be intuitively plausible, but really requires proof. Prove that the equation is correct. To simplify the editing, right-hand side of equation , we first note that we can apply rule 2 to the second term on the right-hand side, obtaining . Of Oxford. To check this explicitly, note that the condition for rule 2 to apply is that . We already saw the graph above, and, indeed, is d-separated from in that graph, since the only path from to editing is blocked at . As a result, we have: At this point in the presentation, I’m going to speed the discussion up, telling you what rule of the calculus to thesis writing help apply at each step, but not going through the editing, process of explicitly checking that the university archive, conditions of the rule hold. (If you’re doing a close read, you may wish to editing check the conditions, however.) The next thing we do is to apply rule 2 to cover business development the first term on the right-hand side of equation , obtaining . We then apply rule 3 to remove the editing, , obtaining . Parents Should For Childhood Essay. Substituting back in gives us:
So this means that we’ve reduced the computation of to the computation of . This doesn’t seem terribly encouraging: we’ve merely substituted the computation of one causal conditional probability for another. Still, let us continue plugging away, and see if we can make progress. The obvious first thing to try is to apply rule 2 or rule 3 to simplify . Unfortunately, though not terribly surprisingly, neither rule applies. So what do we do? Well, in a repeat of editing, our strategy above, we again condition on the other variable we have available to us, in this case : Now we’re cooking!
Rule 2 lets us simplify the first term to , while rule 3 lets us simplify the second term to , and so we have . To substitute this expression back into equation  it helps to change the summation index from to , since otherwise we would have a duplicate summation index. This gives us: This is the promised expression for (i.e., for probabilities like , assuming the causal model above) in terms of quantities which may be observed directly from experimental data, and which don’t require intervention to letter for international do a randomized, controlled experiment. Once is editing determined, we can compare it against . If is larger than then we can conclude that smoking does, indeed, play a causal role in cancer. Something that bugs me about the derivation of equation  is that I don’t really know how to “see through” the calculations. Yes, it all works out in the end, and it’s easy enough to follow along. Yet that’s not the same as having a deep understanding. Too many basic questions remain unanswered: Why did we have to condition as we did in cover, the calculation?
Was there some other way we could have proceeded? What would have happeed if we’d conditioned on the value of the hidden variable? (This is not obviously the wrong thing to editing do: maybe the hidden variable would ultimately drop out of the parents be blamed obesity, calculation). Why is it possible to compute causal probabilities in this model, but not (as we shall see) in the model without tar? Ideally, a deeper understanding would make the answers to editing some or all of these questions much more obvious. Why is introductions dissertations it so much easier to compute than in the model above? Is there some way we could have seen that this would be the editing, case, without needing to go through a detailed computation? Suppose we have a causal model , with a subset of vertices for which all conditional probabilities are known.
Is it possible to give a simple characterization of for which subsets and of vertices it is possible to the hand that you essays compute using just the conditional probabilities from ? Unfortunately, I don’t know what the experimentally observed probabilities are in the smoking-tar-cancer case. Editing. If anyone does, I’d be interested to know. In lieu of actual data, I’ll use some toy model data suggested by Pearl; the data is quite unrealistic, but nonetheless interesting as an illustration of the use of equation . The toy model data is as follows: (1) 47.5 percent of the population are nonsmokers with no tar in their lungs, and 10 percent of these get cancer. (2) 2.5 percent are smokers with no tar, and dissertations, 90 percent get cancer. (3) 2.5 percent are nonsmokers with tar, and editing, 5 percent get cancer. (4) 47.5 percent are smokers with tar, and 85 percent get cancer. In this case, we get:
By contrast, percent, and so if this data was correct (obviously it’s not even close) it would show that smoking actually somewhat reduces a person’s chance of school bully, getting lung cancer. This is despite the fact that percent, and editing, so a naive approach to writing help dhabi causality based on correlations alone would suggest that smoking causes cancer. In fact, in this imagined world smoking might actually be useable as a preventative treatment for cancer! Obviously this isn’t truly the case, but it does illustrate the power of this method of editing, analysis. Summing up the general lesson of the good introductions dissertations, smoking-cancer example, suppose we have two competing hypotheses for the causal origin of some effect in a system, A causes C or B causes C, say.
Then we should try to construct a realistic causal model which includes both hypotheses, and then use the editing, causal calculus to attempt to distinguish the relative influence of the two causal factors, on the basis of experimentally accessible data. Incidentally, the should for childhood obesity, kind of analysis of smoking we did above obviously wasn’t done back in the 1960s. I don’t actually know how causality was established over the protestations that correlation doesn’t impy causation. But it’s not difficult to think of ways you might have come up with truly convincing evidence that smoking was a causal factor. One way would have been to look at the incidence of editing, lung cancer in populations where smoking had only recently been introduced. Suppose, for example, that cigarettes had just been introduced into the (fictional) country of Nicotinia, and that this had been quickly followed by you essays a rapid increase in rates of editing, lung cancer. Introductions. If this pattern was seen across many new markets then it would be very difficult to argue that lung cancer was being caused solely by some pre-existing factor in the population. Construct toy model data where smoking increases a person’s chance of editing, getting lung cancer. Let’s leave this model of smoking and lung cancer, and about bully, come back to our original model of smoking and lung cancer: What would have happened if we’d tried to use the causal calculus to analyse this model? I won’t go through all the details, but you can easily check that whatever rule you try to apply you quickly run into editing, a dead end.
And so the causal calculus doesn’t seem to be any help in analysing this problem. This example illustrates some of the limitations of the spm speech school, causal calculus. Editing. In order to compute we needed to assume a causal model with a particular structure: While this model is plausible, it is parents essay not beyond reproach. You could, for editing example, criticise it by saying that it is not the presence of tar deposits in the lungs that causes cancer, but maybe some other factor, perhaps something that is currently unknown. This might lead us to bite the hand feeds you essays consider a causal model with a revised structure: So we could try instead to use the causal calculus to analyse this new model. I haven’t gone through this exercise, but I strongly suspect that doing so we wouldn’t be able to use the rules of the editing, causal calculus to compute the relevant probabilities. The intuition behind this suspicion is that we can imagine a world in which the tar may be a spurious side-effect of university thesis archive, smoking that is in fact entirely unrelated to lung cancer. Editing. What causes lung cancer is really an entirely different mechanism, but we couldn’t distinguish the two from the statistics alone.
The point of this isn’t to say that the parents should be blamed for childhood obesity, causal calculus is useless. It’s remarkable that we can plausibly get information about the outcome of a randomized controlled experiment without actually doing anything like that experiment. But there are limitations. To get that information we needed to make some presumptions about the causal structure in the system. Those presumptions are plausible, but not logically inevitable. If someone questions the presumptions then it may be necessary to editing revise the model, perhaps adopting a more sophisticated causal model. One can then use the causal calculus to thesis dhabi attempt to analyse that more sophisticated model, but we are not guaranteed success. It would be interesting to understand systematically when this will be possible and when it will not be. The following problems start to get at some of the issues involved. Is it possible to editing make a more precise statement than “the causal calculus doesn’t seem to be any help” for the original smoking-cancer model? Given a probability distribution over some random variables, it would be useful to have a classification theorem describing all the causal models in which those random variables could appear.
Extending the last problem, it’d be good to have an good, algorithm to answer questions like: in the space of all possible causal models consistent with a given set of observed probabilities, what can we say about the possible causal probabilities? It would also be useful to be able to input to editing the algorithm some constraints on the causal models, representing knowledge we’re already sure of. In real-world experiments there are many practical issues that must be addressed to design a realiable randomized, controlled experiment. Feeds You Essays And Provocations. These issues include selection bias, blinding, and editing, many others. There is an entire field of bite that you essays and provocations, experimental design devoted to addressing such issues. By comparison, my description of causal inference ignores many of these practical issues. Editing. Can we integrate the best thinking on experimental design with ideas such as causal conditional probabilities and cover for international development, the causal calculus? From a pedagogical point of view, I wonder if it might have been better to work fully through the smoking-cancer example before getting to the abstract statement of the rules of the causal calculus. Those rules can all be explained and motivated quite nicely in editing, the context of the smoking-cancer example, and that may help in understanding. I’ve described just a tiny fraction of the work on of oxford, causality that is now going on.
My impression as an admittedly non-expert outsider to the field is that this is an editing, exceptionally fertile field which is developing rapidly and giving rise to many fascinating applications. Dissertations. Over the next few decades I expect the theory of causality will mature, and be integrated into editing, the foundations of disciplines ranging from economics to medicine to social policy. Causal discovery: One question I’d like to understand better is thesis writing help in abu how to editing discover causal structures inside existing data sets. After all, human beings do a pretty good (though far from perfect) job at figuring out causal models from their observation of the world. I’d like to better understand how to use computers to automatically discover such causal models. I understand that there is already quite a literature on the automated discovery of causal models, but I haven’t yet looked in thesis writing, much depth at that literature. I may come back to it in a future post. I’m particularly fascinated by the idea of extracting causal models from very large unstructured data sets. The KnowItAll group at the University of Washington (see Oren Etzioni on editing, Google Plus) have done fascinating work on a related but (probably) easier problem, the problem of open information extraction. This means taking an unstructured information source (like the web), and cover business, using it to editing extract facts about the real world. For instance, using the school bully, web one would like computers to be able to learn facts like “Barack Obama is President of the United States”, without needing a human to feed it that information.
One of the things that makes this task challenging is editing all the misleading and difficult-to-understand information out on the web. For instance, there are also webpages saying “George Bush is President of the United States”, which was probably true at that feeds you essays the time the editing, pages were written, but which is now misleading. Thesis Archive. We can find webpages which state things like “[Let’s imagine] Steve Jobs is President of the United States“; it’s a difficult task for an unsupervised algorithm to figure out how to interpret that “Let’s imagine”. What the KnowItAll team have done is editing made progress on figuring out how to learn facts in such a rich but uncontrolled environment. What I’m wondering is university of oxford whether such techniques can be adapted to extract causal models from data?
It’d be fascinating if so, because of editing, course humans don’t just reason with facts, they also reason with (informal) causal models that relate those facts. Parents Should Be Blamed Essay. Perhaps causal models or a similar concept may be a good way of representing some crucial part of our knowledge of the world. What systematic causal fallacies do human beings suffer from? We certainly often make mistakes in editing, the causal models we extract from university of oxford, our observations of the world – one example is that we often do assume that correlation implies causation, even when that’s not true – and it’d be nice to understand what systematic biases we have. Editing. Humans aren’t just good with facts and causal models. We’re also really good at juggling multiple causal models, testing them against one another, finding problems and inconsistencies, and dissertations, making adjustments and integrating the results of those models, even when the results conflict. In essence, we have a (working, imperfect) theory of how to deal with causal models.
Can we teach machines to editing do this kind of integration of causal models? We know that in our world the sun rising causes the letter business development, rooster to editing crow, but it’s possible to imagine a world in which it is the rooster crowing that causes the sun to cover rise. This could be achieved in editing, a suitably designed virtual world, for example. The reason we believe the first model is should for childhood correct in editing, our world is not intrinsic to the data we have on roosters and sunrise, but rather depends on a much more complex network of introductions, background knowledge. For instance, given what we know about roosters and the sun we can easily come up with plausible causal mechanisms (solar photons impinging on editing, the rooster’s eye, say) by which the sun could cause the rooster to crow. There do not seem to be any similarly plausible causal models in the other direction.
How do we determine what makes a particular causal model plausible or not? How do we determine the class of plausible causal models for a given phenomenon? Can we make this kind of judgement automatically? (This is thesis help in abu dhabi all closely related to the last problem). Continuous-time causality: A peculiarity in my post is that even though we’re talking about causality, and time is presumably important, I’ve avoided any explicit mention of time. Of course, it’s implicitly there: if I’d been a little more precise in specifying my models they’d no doubt be conditioned on events like “smoked at least a pack a day for 10 or more years”.
Of course, this way of putting time into editing, the picture is bite the hand you essays rather coarse-grained. In a lot of practical situations we’re interested in understanding causality in a much more temporally fine-grained way. To explain what I mean, consider a simple model of the relationship between what we eat and editing, our insulin levels: This model represents the fact that what we eat determines our insulin levels, and our insulin levels in bite feeds and provocations, turn play a part in determining how hungry we feel, and thus what we eat. But as a model, it’s quite inadequate.
In fact, there’s a much more complex feedback relationship going on, a constant back-and-forth between what we eat at any given time, and our insulin levels. Ideally, this wouldn’t be represented by a few discrete events, but rather by a causal model that reflects the editing, continual feedback between these possibilities. What I’d like to see developed is a theory of continuous-time causal models, which can address this sort of the hand that you essays and provocations, issue. It would also be useful to extend the editing, calculus to good dissertations continuous spaces of editing, events. Cover Letter. So far as I know, at present the causal calculus doesn’t work with these kinds of ideas. Can we formulate theories like electromagnetism, general relativity and quantum mechanics within the framework of the causal calculus (or some generalization)? Do we learn anything by editing doing so? Other notions of good introductions, causality: A point I’ve glossed over in the post is how the notion of causal influence we’ve been studying relates to other notions of causality. The notion we’ve been exploring is based on the notion of causality that is established by a (hopefully well-designed!) randomized controlled experiment.
To understand what that means, think of what it would mean if we used such an experiment to establish that smoking does, indeed, cause cancer. All this means is editing that in the population being studied , forcing someone to smoke will increase their chance of getting cancer. Now, for the practical matter of setting public health policy, that’s obviously a pretty important notion of causality. Parents For Childhood Obesity Essay. But nothing says that we won’t tomorrow discover some population of people where no such causal influence is editing found. Or perhaps we’ll find a population where smoking actively helps prevent cancer. Both these are entirely possible.
What’s going on is that while our notion of causality is good useful for some purposes, it doesn’t necessarily say anything about the details of an underlying causal mechanism, and it doesn’t tell us how the results will apply to other populations. Editing. In other words, while it’s a useful and important notion of help in abu, causality, it’s not the only way of editing, thinking about causality. Something I’d like to do is to understand better what other notions of causality are useful, and that you essays, how the intervention-based approach we’ve been exploring relates to editing those other approaches. Thanks to Jen Dodd, Rob Dodd, and Rob Spekkens for cover for international many discussions about causality. Especial thanks to Rob Spekkens for editing pointing me toward the epilogue of good introductions dissertations, Pearl’s book, which is what got me hooked on causality! Principal sources and further reading. A readable and stimulating overview of causal inference is the epilogue to Judea Pearl’s book. The epilogue, in turn, is editing based on a survey lecture by Pearl on causal inference.
I highly recommend getting a hold of the book and reading the epilogue; if you cannot do that, I suggest looking over the survey lecture. A draft copy of the first edition of the entire book is bite and provocations available on Pearl’s website. Editing. Unfortunately, the be blamed, draft does not include the full text of the epilogue, only the survey lecture. The lecture is editing still good, though, so you should look at it if you don’t have access to the full text of the epilogue. I’ve also been told good things about the be blamed for childhood obesity essay, book on causality by Spirtes, Glymour and Scheines, but haven’t yet had a chance to editing have a close look at it. An unfortunate aspect of the current post is that it gives the impression that the letter for international business development, theory of causal inference is editing entirely Judea Pearl’s creation. Of course that’s far from the case, a fact which is quite evident from both Pearl’s book, and the Spirtes-Glymour-Scheines book.
However, the particular facets I’ve chosen to focus on are due principally to Pearl and his collaborators: most of the current post is based on chapter 3 and chapter 1 of the hand feeds you essays and provocations, Pearl’s book, as well as a 1994 paper by Pearl, which established many of the key ideas of the causal calculus. Editing. Finally, for an enjoyable and informative discussion of some of the challenges involved in of oxford, understanding causal inference I recommend Jonah Lehrer’s recent article in Wired . Interested in more? Please subscribe to this blog, or follow me on editing, Twitter. You may also enjoy reading my new book about open science, Reinventing Discovery. Do you think there’d be a way to interpret causal structure via geometry, much like we use geometry to express correlation and other patterns in data mining. Bite You Essays. The geometry might have to be something that encodes causality – maybe a manifold with negative signature ? @Suresh – Fascinating idea! No idea if it’s possible, though, the thought never crossed my mind. I guess I think of causal models as having an inherent directionality, due to the dag structure, while most geometries don’t have the editing, same kind of directionality. But maybe there’s some trick to get around that. There’s been plenty of work on the geometry of curved exponential families, and their relation to inference in graphical models. See, as a start, e.g.
Bernd Sturmfels and Lior Pachter also have a pretty good book that touches on a lot of this — Yes, I’m aware of parents for childhood obesity, that work. But the editing, geometry there is a geometry in essay school bully, the parameter space. I don’t think it can be used to capture this kind of causality (at least at editing first glance) I came across this as I was interested in oral thrush. Thesis. The NHS guidance (quite reasonably) states that a high proportion of AIDS patients have thrush. Thrush has many causes and editing, is correlated with use of inhaled steroids. I read the article without a second thought – it seemed correct and balanced. But commenters assumed that thrush had a high probably of being caused by aids and that it was highly irresponsible not to say it could also be caused by steroids. This is a typical example of Bayes – the a priori chance of having AIDS is lower (I think) than being on Oral steroids.
I don’t know the answer. I don’t think the introductions dissertations, human race can eveolve genetically to process probabilities correctly, so it has to be education at an early age! That’s another nice example, and of a type that I suspect often infects policy-making and public discussion. 1. If there’s an alternative . Editing. path from smoking to lung cancer it may be possible to essay about school put bounds on P(cancer|dio(smoking)) even if you can’t compute it exactly. 2. Similar graphs can be constructed for quantum amplitudes instead of (and in addition to) probabilities. Editing. It might be interesting to analyse EPR and other experiments in this way, especially from the point of view of hidden variable models of QM.
Thanks for this very informative post. Let me just make a few comments about essay school bully, your “physics” question: “Can we formulate theories like electromagnetism, general relativity and quantum mechanics within the framework of the causal calculus (or some generalization)? Do we learn anything by doing so?” I have been working on formulating quantum theory in a Bayesian network language, which is an obvious precursor to editing developing a causal calculus for it. Even that problem is not so simple, given that the standard formalism has an assumed causal structure built into that feeds you essays, it, which we need to get rid of before we start. My recent papers with Rob Spekkens are part of an attempt to editing do that.
One lesson that I have learned from this is that we need to get away from the school, usual “initial state+dynamics” way of looking at editing physics in order to fit it into this framework. Any correlations that exist in the initial state have to be modelled explicitly in the causal network because it assumes that the root vertices are independent. Finally, let me just mention that you might be able to get away with a simpler structure for modelling causality in deterministic theories like electromagnetism. Directed acyclic graphs are needed in general in order to model non-Markovian causal processes, but deterministic theories (and unitary evolution in quantum theory) are necessarily Markovian. Therefore, you should be able to get away with just using a poset to model causality in these cases, the corresponding DAG being just the cover for international development, Hasse diagram of the poset. It is much easier to deal with continuous posets than continuous generalizations of graphs, so this could be a good first step.
By the way, this explains why Raphael Sorkin et. al. are able to get away with just using posets in the causal set approach to quantum gravity, because they only care about global unitary evolution. Thanks for the pointer to your work, Matt, it sounds fascinating. Although I’ve chatted with Rob about editing, this, I didn’t realize that you were trying to university of oxford thesis formulate quantum theory in terms of Bayesian networks. (He may well have mentioned it, but I perhaps didn’t understand what he was saying – I hadn’t read Pearl at all at that time – and so forgot.) Nice exposition! Perhaps some notion of “latent surprise” could be relevant.
Adapting from the editing, Wired article you cite, imagine that a candidate drug’s operation has two plausible causal models. The first and writing dhabi, most plausible model is simple. It is used during drug development. Editing. The second-most plausible model is complex (but still plausible if one analyzes it). If that second-most plausible causal model is very different from the first, that could be a “latent surprise” for researchers – a warning that, if their understanding of the drug’s operation changes somewhat, the clinical effects could be profound. In general, if the most plausible few models are close (in the metric of plausibility) yet very different (in the thesis, metric space of causal model similarity), this is a warning of big latent surprises if our understanding shifts a bit. Suppose that, as you speculate, we could automatically “determine the class of plausible causal models for a given phenomenon”. We might then also be able to scan automatically for latent surprises in editing, important systems: scientific, social, financial, policy, and so forth. You mentioned the following: “Obviously, it’d make no sense to have loops in the graph: We can’t have causing causing causing ! At least, not without a time machine.” Loops in good dissertations, causality DAG can be created without time machines as follows. 1. In some distant origin that is not in editing, the history of measurements, A caused B;
4. In Abu. so on and so forth. 5. Over time, A, B, and C have caused other variables due to unknown reasons. So, to the observer, A caused B, which caused C, which (in turn) caused A. This situation could happen in editing, Human History due to lapses in measurement and in Astronomy because the lifetime of the observed (universe) is much longer than the lifetime of the essay spm speech, observer (humans). Thanks for editing this interesting post, which provides a nice concise introduction to causal calculus. There is one interesting aspect to this whole chain of reasoning based on randomized controlled trials as the basis of empirical causality that I haven’t seen discussed yet: a controlled trial assumes that the experimenter is an agent possessing free will, and is thus outside of any causal model. There is a recent tendency in cover letter for international development, the scientific community (see this article for example, and my comments on it) to claim that free will does not exist, and editing, that human behavior is feeds you essays governed entirely by molecular processes (and thus ultimately quantum physics). With that assumption, whatever an editing, experimenter does is merely one more observable in a stochastic network, randomized controlled trials disappear, and be blamed for childhood obesity essay, causal calculus disappears as well. We arrive at the conclusion that the editing, only scientific method to attribute causality relies on the existence of free will as a source of “obvious” causality. But then, as you show, there are causal models from which the experimenter’s intervention can be eliminated. We can thus draw conclusions about causality without assuming the letter for international development, “obvious” source of free will.
I wonder if it is editing possible to state under which conditions a causal model permits this elimination. Rules 2 and 3 are about individual variables, but is there a rule that applies to bite the hand that you essays a complete graph? Thanks for this. Editing. I’ve been spending a lot of time thinking about of oxford archive, Pearl’s book lately and this is by far the most accessible introduction to the material that I have come across. One quick correction. Editing. Close to thesis help the end of your discussion of rule 1 (2 paragraphs before the heading: “the rules of the causal calculus”), you give the editing, equation:
Presumably you mean: Thanks, I’ve corrected it! “Business Week recently ran an spm speech school bully, spoof article pointing out some amusing examples of the dangers of inferring correlation from causation.” Probably you meant the other way around: “amusing examples of the dangers of inferring causation from correlation”? I have enjoyed a lot reading this. I am slightly confused about the wording of the following sentence: where f_j is a function, and Y_j is editing a collection of random variables such that: (a) the Y_j,. are independent of one another for different values of j; and (b) for spm speech each j, Y_j,. is independent of all variables X_k, except when X_k is X_j itself, or a descendant of editing, X_j. The intuition is that the are a collection of auxiliary random variables which inject some extra randomness into X_j (and, through X_j, its descendants), but which are otherwise independent of the bite the hand that you essays, variables in the causal model.
What you mean by that is that for instance in editing, the diagram above the paragraph Y_4,i is not independent of X_3 and X_2? No the Y_4,i’s are independent of X_3 and X_2. The only for international business development way this could fail is if condition (b) is editing met. Of Oxford. That condition tells us that Y_4,i may not be independent of X_k when X_k is X_4 or a descendant. In that particular diagram, X_4 has no descendants, so we merely have Y_4,i not a descendant of X_4. Thanks for writing this up. It was very helpful! Regarding eq , you commented that it wasn’t transparent. If I’m not mistake, you can reduce this to.
which is much more transparent. How do you do this? My mistake. I thought I had marginalized out the x’, but didn’t. one famous place case study where “hidden causality” is notoriously, even fiendishly difficult to isolate and shows the extreme subtlety involved: local hidden variable theories for quantum mechanics. which recently have been brought back from the dead (or maybe semi zombie state) by anderson/brady in a soliton model. more thoughts on that here. it has an editing, aura of unorthodoxy but lets not forget that the thesis writing help in abu, greats have always been enamored with the idea. einstein, schroedinger, ‘t hooft, etcetera. part of the difficulty in QM is the idea of counterintuitive variables that might actually cause the editing, experiment apparatus to good dissertations “measure” or “not measure” (or “click” vs “not click/silent”). this has been called a “conspiracy” for decades. Editing. not sure who invented that description.
Goes into cover letter, causal detection based upon ‘prediction when variable A has been removed’, and why correlation sometimes makes causal detection worse, not better. Imply causation? I think this has been an issue for editing some time now because, frankly, causality cannot be proven. The Hand You Essays And Provocations. What science engages in is probablistic hypothetical inductive empiricism – in short, we can never know causality no matter how much some scientists would like you to believe. Science today is merely a refined scholasticism, that just so happened to editing plague humanity for nearly 2000 years. Not a single person can or has or will prove (analytically) universal causality of Being – to put it in easier terms, someone prove to me gravity will exist next Tuesday… Interesting article overall, but I disagree with this statement: We can’t have X causing Y causing Z causing Y! In fact, this is called positive feedback loop and is common in essay spm speech school bully, nature. You will find a lot of examples in wikipedia, none of them needs a time machine #128521; I noticed I incorrectly quoted you above, but the point is, loops in causal diagrams are common.
The labels in the diagrams aren’t just for broad classes of phenomena, they’re labels for random variables. A reasonable informal way of thinking is editing that this means you should think of the nodes as referring to writing in abu specific events. Suppose you have a feedback loop: Eating chocolate = causes Mark to gain weight = reduced tolerance for editing glucose = Eating chocolate (etc). Bite The Hand That You Essays And Provocations. The second “Eating chocolate” is actually a later event, which would be associated with a separate random variable, and would have a separate node in a causal diagram. Incidentally, that informal way of thinking – nodes as specific events in time – isn’t the editing, full story.
You really need to understand the technical definition of a random variable. But this informal approach conveys the gist of parents, what’s going on. In , I’m confused how to expand the right side; I don’t see where I can get the values for pa(Xj). I’m trying to expand the basic cancer-smoking-hidden model in terms of basic probabilities, and I can only get as far as P(gets cancer | do(smokes)) = P(gets cancer, smokes) / P(smokes | pa(Smoke)). (My end goal is to editing see if I can use  to expand the cancer-smoking-tar-hidden model and obtain the same result that you did, but without using the bite feeds you essays and provocations, causal calculus.) pa(.) is just used to denote the parents of a node (or collection of nodes) in the causal graph. I had previously heard one of Pearl’s talks and I took a course in graphical models, but I really understood the Pearl’s ideas better after reading your post. Editing. Thanks. Hello, thanks for this nice explanation of Pearl’s al. theory. But there is something I can’t grasp in spite of reading Pearl’s lecture slides or some parts of his papers. When simplifying equation , you say (as Pearl does) that we can apply rule 2 to that feeds and provocations find : p(z|do(x)) = p(z|x) But rule 2 is much more complex than this.
It tells about x,y,z and w. How can you make disappear y and editing, w in writing dhabi, rule 2 ? Is it because w is unobserved ? Is it because pa(y) = x and we can use another relation ? Thanks for your help. Okay, after many readings , I guess I’m now able to answer to myself. In the 1992 paper, Pearl derives three properties from  formula. p(z|do(x)) = p(z|x) iff z_|_ pa(x) | x. which is the case in the example graph.
Though Pearl says that rule 2 is editing equivalent to this property, I think the latter is bite feeds you essays and provocations much more powerful ! I am trying to editing understand your eq. ; when I set up the calculations in a spreadsheet table, I get the following result, namely no difference between P(cancer) and P(cancer|do(smoking)), which is what I intuitively expected. Can you tell me where I went wrong? no tar no smoke 0.1 0.5 0.475 0.95 0.0475. no tar smoke 0.9 0.5 0.025 0.05 0.0225. tar no smoke 0.05 0.5 0.025 0.05 0.00125. tar smoke 0.85 0.5 0.475 0.95 0.40375. Regarding the application of Simpson’s Paradox to the Civil Rights Act and your mention of application to gender bias I would ask, how far can one go in “slicing and dicing”? How often is this an exercise in merely seeking an for international business, outcome that supports one’s pre-existing bias? For instance, can I go further and split the “north” into east and west of the Mississippi? Suppose this how the the votes came out with this further split (recall we had DemNorth(145/154), RepNorth(138/162)):
North-East: Dem(129/134 .966) North-West: Dem(16/20 = .8) Rep(109/132 .825) Now we have three regions, NorthEast, NorthWest, and South and editing, the republican % was higher in two out of three. Given the Rep(0/10) in the south that can’t be sliced in any manner to seek a favorable outcome for a rep analyst, but you get my point. I just quickly jotted down a few trials to come up with this example which is not surprising given the initial split into north-south is just a first iteration that demonstrates this is possible. But again I ask, where does the slicing and dicing stop in bite that feeds you essays and provocations, such an analysis? Usually with these sorts of political and judicial analyses, those things that involve human motivations, it usually stops where the editing, desired outcome is achieved – and the best part is – one can claim it was scientific and mathematical so is indisputable! The analyst can say under oath and with a straight face,”I lay the numbers before you and thesis in abu dhabi, the numbers don’t lie.” But just what do the editing, numbers tell us? Your threshold “being Republican, rather than Democrat, was an important factor in causing someone to vote for cover business the Civil Rights Act” is also subjective – as it must be in dealing with human motivations, e.g. what is ‘important’?, what is ‘causing’? One could note the 94Dem/10Rep representation from the south, and analyzing the majority of southern voter’s motivations at that time conclude that a major reason for editing the big Dem majority in introductions dissertations, that region was in part caused by the voter’s view that based on platforms and reputation, being Rep, the losing challenger was most likely in favor of the Civil Rights Act.
In see that in editing, my previous post on “slicing and dissertations, dicing” somehow things got a bit garbled between what I typed in and what displayed. One could derive the details given what did display but here is what I intended regarding the East-West split of the North in the Civil Rights vote split: North-West Dem(16/20)=.80 Rep(109/132),825. I’ve applied Simpson’s Paradox to the North vote split. This is hypothetical, but one could gerrymander a region to demonstrate or refute pretty much whatever one wanted. Sorry I’m a little late to the party… but I’ve been busy doing a lot of work in what I’m calling a “science of editing, conceptual systems” where a conceptual system is a set of interrelated concepts (theories, models, mental models, policies, strategic plans, etc.). My research shows how we can use these kinds of insights to create theories and policies that are more likely to be effective in practical application.
You can access some of archive, my writings at: http://projectfast.org/category/research/articles/ There, i analyze the editing, evolution of a theory of physics from letter development, ancient times through the editing, scientific revolution. Essay Spm Speech About School. By focusing on causal relationships, and concatenated relationships between nodes, we gain rather useful insights into editing, how to create more effective theories and bite the hand feeds, policies. This is important because, within the social sciences, our current theories fail far more often than they succeed. imagine what we might be able to editing accomplish if our economic policies worked twice as well as they do? What about theories of management and psychology? Double the thesis writing help in abu dhabi, effectiveness and watch what happens to editing organizational and mental health! The immediate lesson from the graph of Figure B is that and can tell us something.
about one another, given , if there is a path between and be blamed obesity, where the only collider. is at . In fact, the editing, same phenomenon can occur even in this graph: In the example you gave about the music academy, and Berkson’s paradox, there should be another node in the graph: that X gives information about Y if and only if X and Y have some other (external) connection. The other connection in good introductions, this case is: our intuition that music prodigies are usually disinterested in their other studies. So, you cannot proceed to the principle that when X – Z – Y, X gives information about editing, Y, i.e. that the path is unblocked. The path is only unblocked due to the presence of another path (our personal guess that musical prodigies neglect their other studies). The immediate lesson from the should be blamed for childhood obesity essay, graph of Figure B is that and can tell us something.
about one another, given , if there is a path between and where the only collider. is at . In fact, the same phenomenon can occur even in this graph: In the example you gave about the editing, music academy, and Berkson’s paradox, there should be another node in the graph: X gives information about Y if and only if X and Y have some other (external) connection. The other connection in this case is: our intuitive guess that music prodigies are usually disinterested in their other studies. So, you cannot proceed to the principle that when X – Z Z – Y is blocked.
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14 Skills and Values Employers Seek in editing, Jobseekers. by Randall S. Hansen, Ph.D., and Katharine Hansen, Ph.D. You Essays And Provocations. Job Skills to list on your Resume. Deals with acting in a responsible and fair manner in all your personal and work activities, which is seen as a sign of maturity and self-confidence; avoid being petty. How to editing describe this skill on your resume: Conscientious go-getter who is good, highly organized, dedicated, and committed to professionalism. Employers probably respect personal integrity more than any other value, especially in light of the many recent corporate scandals. How to describe this skill on your resume: Seasoned professional whose honesty and integrity create effective leadership and optimal business relationships. Deals with openness to new ideas and concepts, to working independently or as part of a team, and to carrying out multiple tasks or projects. How to describe this skill on your resume: Highly adaptable, mobile, positive, resilient, patient risk-taker who is open to new ideas.
Employers seek jobseekers who love what they do and will keep at it until they solve the problem and get the job done. How to describe this skill on editing, your resume: Productive worker with solid work ethic who exerts optimal effort in bite the hand, successfully completing tasks. 5.Dependability/Reliability/Responsibility. There#8217;s no question that all employers desire employees who will arrive to editing work every day? on time? and ready to work, and who will take responsibility for their actions. Thesis Help. How to describe this skill on your resume: Dependable, responsible contributor committed to excellence and success. Employers want employees who will have a strong devotion to the company? even at times when the company is not necessarily loyal to its employees. How to describe this skill on your resume: Loyal and dedicated manager with an excellent work record.
7.Positive Attitude/Motivation/Energy/Passion. The jobseekers who get hired and the employees who get promoted are the ones with drive and passion? and editing who demonstrate this enthusiasm through their words and actions. How to describe this skill on bite the hand you essays, your resume: Energetic performer consistently cited for unbridled passion for work, sunny disposition, and upbeat, positive attitude. Look at it this way: if you don#8217;t believe in yourself, in your unique mix of skills, education, and abilities, why should a prospective employer? Be confident in yourself and what you can offer employers. Editing. How to describe this skill on your resume: Confident, hard-working employee who is committed to cover letter for international business development achieving excellence.
9.Self-Motivated/Ability to editing Work Without Supervision. While teamwork is always mentioned as an important skill, so is the ability to work independently, with minimal supervision. How to describe this skill on your resume: Highly motivated self-starter who takes initiative with minimal supervision. No matter what your age, no matter how much experience you have, you should always be willing to learn a new skill or technique. Jobs are constantly changing and evolving, and you must show an letter business development openness to editing grow and learn with that change. How to describe this skill on your resume: Enthusiastic, knowledge-hungry learner, eager to meet challenges and quickly assimilate new concepts. While there is some debate about whether leadership is something people are born with, these skills deal with your ability to take charge and manage your co-workers. How to describe this skill on help in abu dhabi, your resume: Goal-driven leader who maintains a productive climate and confidently motivates, mobilizes, and coaches employees to meet high-performance standards. 12.Multicultural Sensitivity/Awareness. There is possibly no bigger issue in the workplace than diversity, and jobseekers must demonstrate a sensitivity and editing awareness to other people and cultures.
How to describe this skill on your resume: Personable professional whose strengths include cultural sensitivity and an ability to build rapport with a diverse workforce in multicultural settings. Deals with your ability to letter for international development design, plan, organize, and implement projects and tasks within an allotted timeframe. Also, involves goal-setting. How to editing describe this skill on your resume: Results-driven achiever with exemplary planning and organizational skills, along with a high degree of detail orientation. Because so many jobs involve working in one or more work-groups, you must have the ability to work with others in a professional manner while attempting to achieve a common goal. How to thesis archive describe this skill on your resume: Resourceful team player who excels at building trusting relationships with customers and colleagues. Final Thoughts on Employment Skills and Values. Editing. Employability skills and university personal values are the editing critical tools and traits you need to succeed in the workplace? and they are all elements that you can learn, cultivate, develop, and maintain over good introductions your lifetime. Once you have identified the sought-after skills and values and assessed the editing degree to which you possess them, begin to market them by building them into your resume, cover letter, and interview answers) for essay bully job-search success. See also our Transferable Job Skills for Jobseekers.Click here to begin building your own resume!
More Information about Employability Skills: Skills Employers Seek, reporting on editing, annual results from the National Association of should for childhood obesity essay, Colleges and Employers (NACE) survey of employers to determine the top 10 personal qualities/skills employers seek. From the Career Development Center at Binghamton University. Editing. Skills Employers Seek, from Loughborough University. Skills Employers Seek, from Psych Web Top 10 Soft Skills in Demand, from LiveCareer Resume Skills Section, from LiveCareer. Building Tools That Build Better Work Lives. Since 2005, LiveCareer’s team of career coaches, certified resume writers, and savvy technologists have been developing career tools that have helped over 10 million users build stronger resumes, write more persuasive cover letters, and develop better interview skills. Use our free samples, templates, and writing guides and our easy-to-use resume builder software to help land the job you want. Dr. Randall S. Hansen.
Dr. Randall S. Hansen is founder of Quintessential Careers, one of the oldest and most comprehensive career development sites on the Web, as well CEO of EmpoweringSites.com. He is also founder of and provocations, MyCollegeSuccessStory.com and EnhanceMyVocabulary.com. He is publisher of Quintessential Careers Press, including the Quintessential Careers electronic newsletter, QuintZine. Dr. Hansen is also a published author, with several books, chapters in books, and hundreds of articles. He’s often quoted in the media and conducts empowering workshops around the country. Finally, Dr. Hansen is also an educator, having taught at the college level for more than 15 years. Visit his personal Website or reach him by email at firstname.lastname@example.org.
Check out editing Dr. Hansen on GooglePlus. That Feeds You Essays And Provocations. Katharine Hansen, Ph.D., creative director and associate publisher of Quintessential Careers, is an educator, author, and blogger who provides content for editing Quintessential Careers, edits QuintZine, an electronic newsletter for jobseekers, and thesis writing blogs about storytelling in the job search at A Storied Career. Katharine, who earned her PhD in organizational behavior from Union Institute University, Cincinnati, OH, is author of Dynamic Cover Letters for New Graduates and A Foot in the Door: Networking Your Way into the Hidden Job Market (both published by Ten Speed Press), as well as Top Notch Executive Resumes (Career Press); and with Randall S. Hansen, Ph.D., Dynamic Cover Letters, Write Your Way to a Higher GPA (Ten Speed), and The Complete Idiot’s Guide to Study Skills (Alpha). Editing. Visit her personal Website or reach her by e-mail at email@example.com. Check out Dr. Hansen on GooglePlus. Parents Should Obesity. I AM A CAREER CHANGER This page is your key source for all things career-change related. You#8217;ll find some great free career-change tools and resources. Changing careers can be traumatic, especially if you have been in your current career for a long time, but you do not have to go through the process alone or  Quintessential Careers: Career and editing Job-Hunting Blog. Quintessential Careers: Career and parents obesity essay Job-Hunting Blog Career and job-search news, trends, and scoops for editing job-seekers, compiled by the staff of Quintessential Careers.The Quintessential Careers Blog has moved!!
These pages remain as an archive of our previous blog posts. Business. Please check out the new and editing improved Quintessential Careers Blog for writing help in abu dhabi Job-Seekers and Careerists. Interview Advice Job  The Quintessential Directory of editing, Company Career Centers. The Quintessential Directory of Company Career Centers Where job-seekers can go directly to the job/career/employment section of a specific employer#8217;s Website.Because more and university thesis more companies are developing career and employment centers on editing, their corporate Websites, Quintessential Careers has developed this directory, which allows you to go straight to the career and the hand that feeds employment section of the  Quintessential Careers: I am a Career Coach or Counselor. The Quintessential Directory of Company Career Centers Where job-seekers can go directly to the job/career/employment section of editing, a specific employer#8217;s Website.Because more and more companies are developing career and employment centers on their corporate Websites, Quintessential Careers has developed this directory, which allows you to should be blamed go straight to the career and editing employment section of the  Mighty Recruiter Mighty Recruiter. Customer Service Customer Service.
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Examples Competency Goal 2 Essays and editing, Research Papers. CAROL HAMM COMPETENCY GOAL . 2 TO ADVANCE PHYSICAL COMPETENCY FUNCTIONAL AREA 1 PHYSICAL I PROMOTE PHYSICAL ACTIVITIES IN MY CLASSROOM DAILY AND LESSON. Childhood , Learning , Nationalism 414 Words | 5 Pages. Competency Goal 1 To Establish and letter for international business, maintain a safe, healthy, learning environment Functional Area 1: Safe To reduce injury I . always use the saying if you can predict something might happen then prevent it before. Editing? Children do not understand that there are dangers all around them, whether its the toys they play with, other kids and thesis writing in abu, their belonging or the equipment that they play on. The important goals that I have are always to editing, put the children's safety, happiness and their needs first. The best. Bathroom , Child , English-language films 857 Words | 3 Pages. Examples of essay about school bully, Competencies A competency is a quality that a company believes is editing desirable for parents should be blamed obesity essay its employees to have. . Competencies can be global or specific. Editing? Global competencies are broad in nature, and are qualities that all employees within an organization should have. Specific competencies are qualities that are exclusive to a position or field within the organization.
Competency Skills Most job-seekers wish they could unlock the secret formula to winning the hearts and minds of employers. What. Competence , Cover letter , Employment 1164 Words | 4 Pages. COMPETENCY GOAL I TO ESTABLISH AND MAINTAIN A SAFE, HEALTHY LEARNING ENVIRONMENT. A Safe Environment In order for the hand that feeds you essays and provocations children . to feel safe away from home they need a safe environment where they can learn and play comfortably. I am always alert and editing, continuously observe the children at all times. Children must never be left unsupervised. In the class, safety precautions such as checking all tables and chairs for damage and sharp edges is done weekly. All electrical outlets that are not being used. Child , Childhood , Classroom 2091 Words | 7 Pages.
Competency Goal 1 for in abu Infants and Toddlers. safe, healthy learning environment. 2 . Editing? To advance physical and parents should for childhood obesity essay, intellectual competence. 3. To support social and . Editing? emotional development and to provide positive guidance. 4. To establish positive and productive relationships with families. 5. To ensure a well-run, purposeful program responsive to participant needs. 6. To maintain a commitment to should be blamed essay, professionalism. Editing? Competency Goal I To establish and maintain a safe, healthy.
Child , Child abuse , Competence 292 Words | 3 Pages. Preschool Caregivers in Center-Based Programs Table 1: CDA Competency Goals and Functional Areas CDA COMPETENCY . Parents Should Obesity Essay? GOALS /. To establish and main-tain a safe, healthy learning environment FUNCTIONAL AREAS LSafe 2 . Healthy DEFINITIONS Candidate provides a safe environment and editing, teaches children safe practices to prevent and reduce injuries. Candidate provides an environment that promotes health and prevents illness, and teaches children about good nutrition and practices that promote. Childhood , Competence , Developmental psychology 542 Words | 3 Pages. University of Phoenix Material SMART Goals Part A: Reflect on thesis archive your results from the Career Interest Profiler Activity and the Career Plan . Building Activity: Competencies . Building on editing your strengths and parents essay, weaknesses, create five SMART goals to editing, help you with your personal academic and introductions dissertations, career journey. Resource: University of Phoenix Material: Goal Setting Example : Take a writing workshop in the next 2 to 3 weeks to help me improve my writing skills in order to successfully communicate.
Career , Goal , Help 293 Words | 3 Pages. Competency Goal VI To maintain a commitment to editing, professionalism Function Area 13: Professionalism As a professional, I promote . The Hand That Feeds You Essays? child development, learning and build family and editing, community relationships. University Thesis Archive? I continue to attend professional development sessions and editing, read articles about early childhood. That Feeds? I am a continuous, collaborative learner who demonstrates knowledge in editing, my field from all types of sources. The Philadelphia School District provides the Head Start staff with many in-service training programs. Child , Childhood , Developmental psychology 971 Words | 3 Pages.
SMART Goals Part A: Reflect on your results from the Career Interest Profiler Activity and spm speech school bully, the Career Plan Building Activity: . Competencies . Editing? Building on thesis writing help in abu dhabi your strengths and weaknesses, create five SMART goals to help you with your personal academic and career journey. Resource: University of Phoenix Material: Goal Setting Example : Take a writing workshop in the next 2 to 3 weeks to help me improve my writing skills in order to editing, successfully communicate with others. Of Oxford Archive? 1. Earn My Bachelor’s. Academic degree , Goal 742 Words | 4 Pages. Presents COMPTENCY MAPPING 1 Day Workshop on SK’s The Strategy of editing, Developing Competency Based Management Systems in Organisations . skills behaviour attitude knowledge success competency 16 November 2013 Hotel Green Park, Hyderabad INTRODUCTION Competency mapping is thesis a framework of editing, knowledge, skills and characteristics needed to effectively perform a role in thesis, an organization.
This helps organization immensely in bringing clarity in editing, all HR processes. All critical. Andhra Pradesh , Competence , Four stages of competence 637 Words | 5 Pages. ? Competency Goal 2 - Physical Intellectual Competence: ? Bridget - CheekyMommaof2, Yahoo! Contributor . University Of Oxford Archive? NetworkJun 9, 2009 Contribute content like this. Start Here. Editing? More: Competence Intellectual Competency Manipulatives Share on Facebook Share on Twitter Print Flag Close 14 Helpful? Post a comment To advance physical and intellectual competence. It is essay school essential to the growth and development of every child to advance their physical and editing, intellectual needs. There are numerous. Competence , Four stages of competence , Game 292 Words | 2 Pages.
? Competency Goal 2 Functional Area 4: Physical I promote physical activities in my classroom daily. We do music . and movement different songs like Pirate Ship, Bear Hunt and archive, the Dancing Machine. All provide a wide range of movement to promote physical health. During outdoor play children are given opportunities , to play with balls, climbing up and down the stairs to the slide, hopping, skipping, jumping and balancing on a rail. Children need opportunities to editing, develop their body strength. Pulling. Child , Cognition , Communication 332 Words | 1 Pages. Title: “The Level of Motivation and bite feeds you essays, the Teaching Competency of High School Teachers in editing, Laguna College of Business and Arts, A.Y.
2012-2013. Letter For International Business Development? . CHAPTER I INTRODUCTION Profession is a calling requiring specialized knowledge and often long and editing, intensive preparation including instruction in skills and methods as well as in the scientific, historical or scholarly principles underlying such skills and parents for childhood obesity essay, methods. A person who has a profession can be called a professional. It has different types of. Abraham Maslow , Educational psychology , Hygiene factors 1333 Words | 6 Pages. Competency Goal II To advance physical and intellectual competence To advance physical and intellectual competence I provide . various amounts of equipment and activities that help promote the physical development of children. I do this by editing, offering activities that will allow children to use their small muscles and university of oxford archive, large muscles. Children need these types of activities so they can become familiar about how to use their muscles and to develop body strength. Activities such as throwing things, rolling. Cognition , Competence , Developmental psychology 421 Words | 2 Pages. Competency Goal II To Advance Physical and intellectual Competence Functional Area 4: Physical In today's society there is an editing, . increase in obese children. Parents Be Blamed? I make sure that each child gets the proper exercise that he or she needs.
I try to teach the children to stay healthy by getting the proper exercise that is needed for them. I take the children outside everyday if it’s not too hot, or too cold. I practice forms of yoga in the morning and the evenings with the children. This is. Activity , Childhood , Cognition 955 Words | 3 Pages. COMPETENCY MAPPING ABSTRACT: The term competency is quite generic and related to human behavior and psychology. . Editing? Competency is often defined as a quality of being adequately qualified, intellectually able and emotionally stable. The concept of competency is basically used to define a capability of essay spm speech about bully, being able. Competency mapping is a psychological test that helps the human resource management department to editing, test the cover letter business, competence or competency of an employee or a potential employee.
It must be noted. Competence , Emotional intelligence , Four stages of competence 2026 Words | 7 Pages. competencies Prepare a list of 5 competencies for your own position. Explain why you choose these competencies and . what do they include behaviorally? Competencies are the core elements of talent management practices that are the editing, demonstrable and measurable knowledge, skills, behaviours, personal characteristics that is essay school bully related with the editing, success of the university of oxford archive, job. Choosing the editing, right competencies allows employers to: • Plan how they will organize and develop their workforce. • Determine which job. Behavior , Education , Human behavior 837 Words | 3 Pages. Competency Goal 1 Functional Area 1: Safe. Candidate provides a safe environment to parents for childhood, prevent and reduce injuries.
To . establish and maintain a safe, healthy learning environment my number one goal is to keep my children safe while they are in my care. Editing? I always make sure that I am aware of all the university of oxford thesis archive, children in my room. I make sure that all plug outlets covered with protectors. All toys are age appropriate for my children. I practice the fire, tornado, and earthquake drill once a month with my children. Fine motor skill , Hand , Health 461 Words | 2 Pages. ?University of Phoenix Material SMART Goals Part A: Reflect on your results from the Career Interest Profiler Activity and the Career . Plan Building Activity: Competencies . Building on your strengths and weaknesses, create five SMART goals to help you with your personal academic and editing, career journey. Resource: University of Phoenix Material: Goal Setting Example : Take a writing workshop in the next 2 to 3 weeks to help me improve my writing skills in order to successfully communicate with. Essay , Goal , Writing 950 Words | 4 Pages. CDA Goal 2 - Competency Statement.
I believe toddlers are very curious and want to explore the world around them. . For International? They are always excited to move their body parts like hand, legs, eyes etc. to explore and discover things around their environment. At this stage they are also beginning to refine their small muscle to control and develop their eye and hand coordination. At young age the children's appreciate growth in their physical abilities. A toddler enjoys rolling a ball back and forth with their. Child development , Childhood , Developmental psychology 768 Words | 2 Pages. Emotional Intelligence and Competencies. guiding awareness of their values and goals 2 . Accurate Self-Assessment -- People with this competence: Are aware of editing, their . strengths and weaknesses Reflective, learning from university experience Open to candid feedback, new perspectives, continuous learning, and self-development Able to show a sense of humor and perspective about themselves BLIND SPOTS: Blind Ambition-need to editing, win or be right at any cost Unrealistic Goals - sets overly ambitious, unattainable goals for group Relentless Striving-.
Competence , Skill 1047 Words | 4 Pages. Competency Based Performance Management. the first part of the project, am trying to study Competency Based Performance Management as a tool for “Performance Management” which could . drastically minimize the gap between expectations and actual performance. Competency based performance management is especially used by many organizations when it’s difficult to measure the performance of the good introductions dissertations, employees objectively. Here, in the second part of the project contains an analysis of how competency based performance management has been carried out. Competence , Evaluation , Management 1919 Words | 7 Pages.
Core Competencies and Competitive Advantages. Core Competencies A core competency is a concept in management theory originally advocated by CK Prahalad, and Gary Hamel, two . business book writers. Editing? In their view a core competency is university of oxford a specific factor that a business sees as being central to the way it, or its employees, works. It fulfills three key criteria: 1. Editing? It is not easy for competitors to imitate. 2 . Bite Feeds And Provocations? It can be re-used widely for many products and markets. 3. Editing? It must contribute to university, the end consumer's experienced benefits. C.K.
Prahalad. Business , C. K. Prahalad , Core competency 1693 Words | 7 Pages. sliding. The large sand pit allows for shoveling sand from one bucket to another and a place to build roads for editing the tractors. My activities for developing . fine motor skills are cutting with scissors in combination with sequencing events in order. Essay Spm Speech? For example , four squares sequenced on how to plant and editing, grow a plant. Daily, we will work daily on obesity essay writing their names, learning the letters that are in editing, their names, and holding the pencil correctly. I encourage my students that being ready for kindergarten. Educational psychology , Feeling , Learning 517 Words | 2 Pages. Competency Goal I: Candidate provide safe environment to prevent and reduce injuries. Functional Area 1: Safety As a . teacher I believe safety is one of the university of oxford thesis, most important things to give to a child besides health and education.
A child and adult should feel safe as soon as they walk into the building in the morning. I practice safety in my classroom by editing, placing items for that feeds and provocations children at their reach and all other items out of their sight. Out of sight out of mind! I cover all. Cardiopulmonary resuscitation , Child , Childhood 386 Words | 3 Pages. Competency Goal II To advance physical development and intellectual competence Functional Area: Physical In our program, . we have many opportunities throughout our day to promote physical development. Some of the ways we do this are: * At recess, students have access to playground equipment. ( We have special equipment for editing 3 year olds) They climb, slide, run, jump, skip, gallop, balance, etc. * At recess, our students like to play games such as Tag, Follow the Leader or Hide and Seek. Cognition , Game , Play 506 Words | 2 Pages.
Review this week’s resources (for example SMART criteria, and the Career Plan Building Activities). Respond to the following in 50 to 100 . words each: 1. Describe one academic goal that you have created using the SMART criteria. How do SMART criteria contribute to your academic goals ?. One academic goal I have created using the cover development, SMART is getting my degree in business management, the reason is this goal has a lot to do with my professional goal . The main goals in my life are academic and professional. Academic degree , Grammar , Grammatical number 830 Words | 3 Pages. and intellectual needs. There are numerous ways to encourage this type of development. I offer a variety of activities that promote the advancement of the . physical and intellectual competence of the children in my care. The following are a few examples : INFANTS: I often play This Little Piggy with them.
This simple game helps them to develop muscle groups, a sense of movement and rhythm and editing, encourages interaction with their caregiver. I ensure that they have the opportunity to explore. English-language films , Game , Infant 370 Words | 2 Pages. ?HRM GOALS HRM has three basic goals , which contribute to achieving management objectives: First goal is . Cover Letter For International? integration of HRM in two senses: integrating HRM into an organization’s corporate strategy, and ensuring an HRM view in the decisions and actions of line managers. Integration in the first sense involves selecting the HRM options consistent with (and which promote) the editing, particular corporate strategy. The option is determined by the type of employee behavior expected (e.g. innovation).
Employment , Goal , Human resource management 765 Words | 3 Pages. Toddlers have a very short attention span, so they need to that feeds and provocations, have numerous activities available to them. They learn by example , so I talk to them . using please and thank you. Playing games is crucial for the development of coordination, balance and gross motor skills. I do this on a daily basis.
We sing songs, read books and talk about shapes, colors, numbers and letters. Competency Goal # 2 - Physical amp; Intellectual Competence: Bridget - CheekyMommaof2, Yahoo! Contributor Network Jun 9, 2009 Contribute. Attention , Childhood , Competence 1583 Words | 10 Pages. University of Phoenix Material SMART Goals Part A: Reflect on your results from the Career Interest Profiler Activity and the Career Plan . Editing? Building Activity: Competencies . Building on your strengths and weaknesses, create five SMART goals to about school bully, help you with your personal academic and career journey. Resource: University of Phoenix Material: Goal Setting Example : Take a writing workshop in the next 2 to 3 weeks to help me improve my writing skills in editing, order to thesis help dhabi, successfully communicate with. Association football , Career , Goal 831 Words | 4 Pages. Out What Is Important to You Overview Tips for editing figuring out what is important to you and spm speech about, making sure your life reflects these priorities. • Step 1: . Thinking about editing your priorities • Step 2 : Setting goals based on the hand that you essays and provocations what’s most important to editing, you • Tips for good introductions setting achievable goals • Put your goals in writing There are two types of priorities: short-term priorities and editing, long-term priorities. For International Business Development? Short-term priorities include your daily to-dos: tasks at work and home, such as finishing a report. Association football , Goal , Life 1010 Words | 4 Pages. Goal From Wikipedia, the free encyclopedia Jump to: navigation, search For other uses, see Goal (disambiguation).
A poster at . the United Nations Headquarters in New York City, New York, USA, showing the editing, Millennium Development Goals A goal is spm speech about bully a desired result a person or a system envisions, plans and commits to editing, achieve a personal or organizational desired end-point in should be blamed for childhood, some sort of editing, assumed development. Many people endeavor to introductions, reach goals within a finite time by editing, setting deadlines. Business? It is editing roughly. Goal , Goal setting , Intrinsic value 1655 Words | 7 Pages. Competency Goal 1 Functional Areas 1,2,3. COMPETENCY GOAL 1 To establish and maintain a safe, healthy learning environment. I keep children healthy by monitoring . their health daily. I ask if they have any boo boos and if so how they got them and I put them on a daily health list, I also do this by carefully observing children, watching for signs of illness, making sure immunizations are up to date. I also keep children healthy by keeping the room clean. Children are informed and writing help, able to make good decisions on how to editing, stay healthy.
Cleanliness , Health , Health care 770 Words | 3 Pages. of my classroom. I can honestly say I set the essay spm speech about bully, “tone” for my classroom. I meet my students with a smile, a song, and a “good morning”…”today is a beautiful . day!”. I lean down to their level and compliment them on any good thing I can find. For example , “You smile is editing just what I needed to see today” or “My favorite color is red and university of oxford archive, you are wearing a red shirt!”. Editing? Every child will have a bad day, just like every adult has a bad day occasionally. It is my job to see the bright side of the situation.
Bad , Behavior , Child 786 Words | 3 Pages. Competency Goal IV To establish positive and productive relationships with families As an early childhood professional, I . support the cover for international business, fact that students, whose parents and/or families are involved in their education, are more successful not only in school but in editing, life. I always try to bite you essays and provocations, encourage families to editing, be a part of their child’s education. I make sure to let families know about my open door policy where they can come into the classroom and stay for letter for international business a short period of time. Child , Childhood , Family 481 Words | 3 Pages. INTERVIEWS COMPETENCY BASED QUESTIONS AN OVERVIEW Presented By Annette Holtom ? What are they? ? Why are they used at interviews? ? How . to answer them ANNETTE HOLTOM/PTLLS 2013 2 UNDERSTANDING COMPETENCY QUESTIONS What are they? • Behavioural Questions – designed to editing, test a specific skill/s Why are they used at interviews? • • • Assess suitability for a job Past behaviour in similar situations Past behaviour – indicator for future behaviour How to answer this style of question . Answer , Behavior , Interrogative word 361 Words | 3 Pages. COMPETENCY MAPPING Dr.J.JAYAKRISHNAN M.B.A., PhD., Associate Professor, Annamalai University. K.PRABAVATHY Research . Scholar, Annamalai University. Abstract In the global economy, firms are becoming more and more aware of the letter, need to have competent employees. Editing? It is not surprising that interest in the concept of competency mapping is rising. Good? Many specific factors are responsible for heightened organizational focus on this critical tool.
One of the main issue, companies are facing. Capacity , Competence , Four stages of competence 1235 Words | 5 Pages. Personal entrepreneurial Competencies. ?PERSONAL ENTREPRENEURIAL COMPETENCIES (PECs) The term ENTREPRENEURIAL COMPETENCIES refers to the key characteristics that . should be possessed by successful entrepreneurs in editing, order to perform entrepreneurial functions effectively. University Of Oxford Thesis? 1. Editing? Initiative The entrepreneur should be able to take actions that go beyond his job requirements and to act faster. He is always ahead of others and able to become a leader in the field of parents obesity essay, business. Examples Does things before being asked or compelled by the situation. Entrepreneur , Entrepreneurship 681 Words | 4 Pages. Phoenix Material Goal Setting Worksheet Review this week’s resources (for example SMART criteria, and the Career Plan . Building Activities). Respond to the following in 50 to 100 words each: 1. Describe one academic goal that you have created using the SMART criteria.
How do SMART criteria contribute to editing, your academic goals ? One academic goal that I have created using the SMART criteria would have be getting my degree. SMART criteria contributes to my academic goal by helping analyze. Goal , Help me , Management 756 Words | 3 Pages. Competency Goal #-To establish and dissertations, maintain a safe, healthy, learning environment. Functional Area #1 – Safe - In order to . help provide an environment which is safe for the children in editing, my care, As a teacher I will do the essay school bully, following to prevent and reduce injuries: I will keep all cleaning supplies and editing, chemicals locked up and out of the children’s reach at all times to bite that feeds, avoid any poisoning or other injuries. I am certified in First Aid, CPR and AED use which gives me the knowledge to handle any. Health , Health care , Hygiene 382 Words | 2 Pages.
A lot is going on in recent times on the issue of editing, competency mapping. For International? A lot of resources spent and consultants invited to editing, do . competency mapping. Increased manpower costs, need for ensuring that competent people man critical positions, and the need to be competitive and university of oxford archive, recognition of the strategic advantages of having good human resources have compelled firms to editing, be more competency driven. In good organisations competency mapping existed already. University? Traditionally HR Directors and their top management. Competence , Four stages of competence , Human resource management 1013 Words | 4 Pages. Difference in editing, competencies between nurses prepared at the associate level versus baccalaureate Keyosha Moore Grand Canyon University . 10/12/2014 An ongoing decision most nurses face today is whether or not to help, go back to school and gain a higher education. Education is a key component in editing, being a successful nurse.
This paper will explain the difference between Associates Degree in Nursing (ADN) and Bachelor of Science in Nursing (BSN). This paper will also describe the disadvantages and advantages. Academic degree , Associate's degree , Bachelor of Science in Nursing 825 Words | 2 Pages. Core Competencies Introduction Core competencies are those capabilities that are critical to bite the hand that, a business achieving competitive . advantage. Editing? The starting point for analysing core competencies is recognising that competition between businesses is as much a race for competence mastery as it is for market position and parents obesity, market power. Senior management cannot focus on all activities of a business and editing, the competencies required to undertake them.
So the university thesis archive, goal is for management to editing, focus attention on competencies. C. K. Prahalad , Computer , Customer relationship management 838 Words | 3 Pages. Chapter 2 Literature Review This chapter presents a brief history regarding social responsibility. Moreover, the nature of . Parents Should? corporate and editing, small business social responsibility will be discussed, as well as the advantages and disadvantages behind socially responsible activities. Brief History Corporate social responsibility is primarily a twentieth-century invention, though its ancient and venerable roots can be traced easily to Biblical sources. The concept is evident. Business , Business ethics , Corporate social responsibility 1788 Words | 6 Pages. career plan details career goals , rather than personal goals . Should Be Blamed For Childhood Obesity? This plan will look at your professional strengths and weaknesses, . the type of editing, company you wish to work for, any knowledge gaps that exist, and your professional resume. Create three to five professional goals (for example , “I want to the hand feeds and provocations, get a job in a health care facility in the emergency services department”), and editing, then complete this outline with those goals in mind. Parents Be Blamed For Childhood Obesity? 1. Goal #1 2 . Goal # 2 3. Goal #3 I. Self-Awareness . Career , Goal , Management 507 Words | 3 Pages.
competency goal #2 infant and toddler. Word Count 95, 124, 144, 126= 489 Competency Goal II To Advance physical and intellectual competence 4. Physical I am . competent in the area of physical activity. My goal is to get every child moving every day. To accomplish my goal I observe each child’s physical capabilities and editing, limitations. I create a purposeful physical activity that is fun and challenging to develop fine and gross motor skills. Physical development is important to a child’s cognitive development. I put a fine and gross. Childhood , Competence , Developmental psychology 511 Words | 2 Pages.
Competencies: Performance Statement Examples. COMPETENCIES Competencies are the measurable or observable knowledge, skills, abilities, and behaviors (KSABs) critical to letter for international, . successful job performance. Choosing the right competencies allows employers to: * Plan how they will organize and editing, develop their workforce. * Determine which job classes best fit their business needs. * Recruit and select the best employees. * Manage and train employees effectively. * Develop staff to fill future vacancies Competency Types . Human resource management , Performance management , Problem solving 7914 Words | 30 Pages. Competency -Based Employee Training 135 · Time is writing help in abu dhabi available to editing, devote to introductions, competency identification, validation, . and modeling. · The training content shelf life is of sufficient length to justify the expense of researching and validating the competency model. · The training population is editing large enough to warrant resource expen- diture. Introductions Dissertations? · Decision makers consider it appropriate to editing, focus on. Competence , Human resource management , Skill 1603 Words | 7 Pages.
suggested a typology of for childhood obesity essay, five types of editing, resources: Material (money and physical capital); Moral (solidarity, support for cover business the movement's . goals ); Social-Organizational (organizational strategies, social networks, bloc recruitment); Human (volunteers, staff, leaders); Cultural (prior activist experience, understanding of the editing, issues, collective action know-how)[ 2 ] Political Opportunity/Political Process- Certain political contexts should be conducive (or representative) for potential social movement. Anthropology , Community building , Ethnomusicology 1424 Words | 5 Pages. Competencies2 Directions: Please review the competencies listed below and bite the hand feeds and provocations, rate your skills. The scale is: Clear Development Need 1 Strength . 2 3 4 5 Character: Displaying high integrity and honesty 4 Avoids saying one thing and doing another (i.e., “walk the talk”) 3 Acts consistently with their words 5Follows through on promises and commitments 4 Models the core values 4Leads by example Personal Capability: Technical and editing, professional expertise 2 Is sought by others for advice and counsel. Problem solving , The Work , Work Records 689 Words | 3 Pages.
Management Competencies – Experience of a Healthcare Manager Context Health care organisations in New Zealand today face similar . Cover For International Business Development? challenges to those in other highly developed countries (1, 2 ). Editing? With growing aging populations and increasing burden of chronic illnesses the demand for publically funded health and disabilities services continues to grow significantly (1). This increase in need coupled with the advancements in technology has driven the costs of providing publically funded healthcare. Health administration , Health care , Health economics 1842 Words | 7 Pages. Introduction ‘‘ Competency -based strategic management is a relatively new way of thinking about thesis how organizations gain high performance for a . significant period of time. Established as a theory in editing, the early 1990s, competence-based strategic management theory explains how organizations can develop sustainable competitive advantage in a systematic and structural way. Parents Be Blamed? The theory of competence-based strategic management is an integrative strategy theory that incorporates economic, organizational and. Competence , Management , Personal life 1180 Words | 4 Pages. Coffee and Distinctive Competencies. Starbucks, the editing, ubiquitous coffee retailer earned high profit and is forecasted to grow. The resources and university thesis, capabilities that have provided Starbuck . distinctive competencies are its unique business model of editing, making the store as a third place between home and work, its ability to own its own stores throughout the world, a strong brand name, decision making based on the ideas provided by employees, for which Starbuck started selling experience through selling ‘third place’, superior customer services.
Balance sheet , Brand , Coffee 1675 Words | 7 Pages. Entrepreneurial Competencies - The EMPRETEC Model Research by McClelland and McBer, funded by spm speech school bully, USAID, has identified 14 personal . entrepreneurial competencies (PECs) which appear to characterize the editing, behaviour of in abu dhabi, successful entrepreneurs. The study, conducted in India, Ecuador and editing, Malawi, also found that these PECs transcended culture, country and continent. These competencies can be grouped into parents be blamed obesity 3 main clusters and the EMPRETEC model has merged some of these competencies to editing, derive 10 PECs. Economics , Entrepreneurship , Finance 517 Words | 4 Pages. Personal Goals Diana Meldon HCS/301 February 12, 2013 Thomas P. Carras, MSEd Personal Goals Setting . goals should not be just for school assignments but should be part of one’s everyday life. Developing goals gives people something to strive toward and allows an individual to keep going forward in life.
Without goals , it is very easy to give up when things get rough. Parents Be Blamed Obesity? According to editing, Bishop, Carter and Kravitis (2011), “Setting goals involves defining what an good introductions, individual is aiming for editing in. Academic degree , Credit cards , Debt 1669 Words | 5 Pages. ? Core Competencies South University Online NSG5000 Week 4 Assignment 2 Vicki Rostis September 12, 2014 Core . Letter For International? Competencies Hamric, Hanson, O’Grady Tracy (2014) define competencies as “a broad area of skillful performance.” Competencies include activities implemented by editing, advanced practice nurses while providing direct patient care and the processes, knowledge and skills used in all aspects of advanced practice. There are seven core competencies associated with the advanced practice.
Advanced practice nurse , Clinical trial , Health care 1065 Words | 6 Pages. Statement of parents should be blamed obesity essay, Competence Competency Goal I To establish and editing, maintain a safe, healthy learning environment Functional Area . 1: Safe In order to maintain a safety environment first, I do a overall inspection indoors and outdoors every morning before starting my day. It is required of me and my fellow co-workers to do a outdoor checklist for help dhabi playground equipment. All tree limbs, broken glass, rocks, or any other foreign object is editing removed. I make sure there are no hazardous items within. Child , Childhood , Conservation movement 443 Words | 2 Pages. eventually will decide to close the plant. Feeds? In his fight to save his plant, a physician, Jonah, helps him in achieving his objectives.
Alex, with the help . of Jonah, finds that the goal of a manufacturing organization and all organizations in general is to make money. Jonah explains the measurements which express the goal of editing, making money in a different way. These measurements are: ? Throughput ? Inventory, and ? Operational expense Throughput is defined as the rate at introductions dissertations, which the editing, system generates. Bottleneck , Choke point , Constraint 1351 Words | 4 Pages. Benchmarks Goal Setting Waldenu Assgn 2. ? Benchmarks and school, Goal Settings for the Adult Learner Veronica L. Barrett Walden University November 12, 2011 Benchmarks and . Editing? Goal Setting for the Adult Learner Introduction To achieve anything in life, one has to set goals . Feeds You Essays? Research has shown that successful people have clear, focused goals and have worked diligently to achieve them. Setting goals allows the editing, learner to identify specific skills and knowledge that are expected for a particular subject.
Goals must be achievable and measureable. Education , Goal , Goal setting 1008 Words | 3 Pages.