andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2097 knowledge-graph by maker-knowledge-mining
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Introduction: Guido Imbens and I write : The statistical and econometrics literature on causality is more focused on “effects of causes” than on “causes of effects.” That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. This has led some researchers to dismiss the search for causes as “cocktail party chatter” that is outside the realm of science. We argue here that the search for causes can be understood within traditional statistical frameworks as a part of model checking and hypothesis generation. We argue that it can make sense to ask questions about the causes of effects, but the answers to these questions will be in terms of effects of causes. We also posted the paper on NBER so I’m hoping it will get some attention from economists. [Again, here's the open link to the paper.] I think what we have here is an important idea linking statistical and econometric models of caus
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1 Guido Imbens and I write : The statistical and econometrics literature on causality is more focused on “effects of causes” than on “causes of effects. [sent-1, score-0.661]
2 ” That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. [sent-2, score-1.074]
3 This has led some researchers to dismiss the search for causes as “cocktail party chatter” that is outside the realm of science. [sent-3, score-1.384]
4 We argue here that the search for causes can be understood within traditional statistical frameworks as a part of model checking and hypothesis generation. [sent-4, score-1.673]
5 We argue that it can make sense to ask questions about the causes of effects, but the answers to these questions will be in terms of effects of causes. [sent-5, score-1.441]
6 We also posted the paper on NBER so I’m hoping it will get some attention from economists. [sent-6, score-0.265]
7 ] I think what we have here is an important idea linking statistical and econometric models of causal inference to how we think about causality more generally. [sent-8, score-0.72]
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same-blog 1 1.0 2097 andrew gelman stats-2013-11-11-Why ask why? Forward causal inference and reverse causal questions
Introduction: Guido Imbens and I write : The statistical and econometrics literature on causality is more focused on “effects of causes” than on “causes of effects.” That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. This has led some researchers to dismiss the search for causes as “cocktail party chatter” that is outside the realm of science. We argue here that the search for causes can be understood within traditional statistical frameworks as a part of model checking and hypothesis generation. We argue that it can make sense to ask questions about the causes of effects, but the answers to these questions will be in terms of effects of causes. We also posted the paper on NBER so I’m hoping it will get some attention from economists. [Again, here's the open link to the paper.] I think what we have here is an important idea linking statistical and econometric models of caus
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Introduction: Macartan Humphreys pointed me to this excellent guide . Here are the 10 items: 1. A causal claim is a statement about what didn’t happen. 2. There is a fundamental problem of causal inference. 3. You can estimate average causal effects even if you cannot observe any individual causal effects. 4. If you know that, on average, A causes B and that B causes C, this does not mean that you know that A causes C. 5. The counterfactual model is all about contribution, not attribution. 6. X can cause Y even if there is no “causal path” connecting X and Y. 7. Correlation is not causation. 8. X can cause Y even if X is not a necessary condition or a sufficient condition for Y. 9. Estimating average causal effects does not require that treatment and control groups are identical. 10. There is no causation without manipulation. The article follows with crisp discussions of each point. My favorite is item #6, not because it’s the most important but because it brings in some real s
Introduction: Consider two broad classes of inferential questions : 1. Forward causal inference . What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth? 2. Reverse causal inference . What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse? When statisticians and econometricians write about causal inference, they focus on forward causal questions. Rubin always told us: Never ask Why? Only ask What if? And, from the econ perspective, causation is typically framed in terms of manipulations: if x had changed by 1, how much would y be expected to change, holding all else constant? But reverse causal questions are important too. They’re a natural way to think (consider the importance of the word “Why”) and are arguably more important than forward questions.
Introduction: Elias Bareinboim asked what I thought about his comment on selection bias in which he referred to a paper by himself and Judea Pearl, “Controlling Selection Bias in Causal Inference.” I replied that I have no problem with what he wrote, but that from my perspective I find it easier to conceptualize such problems in terms of multilevel models. I elaborated on that point in a recent post , “Hierarchical modeling as a framework for extrapolation,” which I think was read by only a few people (I say this because it received only two comments). I don’t think Bareinboim objected to anything I wrote, but like me he is comfortable working within his own framework. He wrote the following to me: In some sense, “not ad hoc” could mean logically consistent. In other words, if one agrees with the assumptions encoded in the model, one must also agree with the conclusions entailed by these assumptions. I am not aware of any other way of doing mathematics. As it turns out, to get causa
Introduction: Psychologists talk about “folk psychology”: ideas that make sense to us about how people think and behave, even if these ideas are not accurate descriptions of reality. And physicists talk about “folk physics” (for example, the idea that a thrown ball falls in a straight line and then suddenly drops, rather than following an approximate parabola). There’s also “folk statistics.” Some of the ideas of folk statistics are so strong that even educated people–even well-known researchers–can make these mistakes. One of the ideas of folk statistics that bothers me a lot is what might be called the “either/or fallacy”: the idea that if there are two possible stories, the truth has to be one or the other. I have often encountered the either/or fallacy in Bayesian statistics, for example the vast literature on “model selection” or “variable selection” or “model averaging” in which it is assumed that one of some pre-specified discrete set of models is the truth, and that this true model
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Introduction: Macartan Humphreys pointed me to this excellent guide . Here are the 10 items: 1. A causal claim is a statement about what didn’t happen. 2. There is a fundamental problem of causal inference. 3. You can estimate average causal effects even if you cannot observe any individual causal effects. 4. If you know that, on average, A causes B and that B causes C, this does not mean that you know that A causes C. 5. The counterfactual model is all about contribution, not attribution. 6. X can cause Y even if there is no “causal path” connecting X and Y. 7. Correlation is not causation. 8. X can cause Y even if X is not a necessary condition or a sufficient condition for Y. 9. Estimating average causal effects does not require that treatment and control groups are identical. 10. There is no causation without manipulation. The article follows with crisp discussions of each point. My favorite is item #6, not because it’s the most important but because it brings in some real s
Introduction: Consider two broad classes of inferential questions : 1. Forward causal inference . What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth? 2. Reverse causal inference . What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse? When statisticians and econometricians write about causal inference, they focus on forward causal questions. Rubin always told us: Never ask Why? Only ask What if? And, from the econ perspective, causation is typically framed in terms of manipulations: if x had changed by 1, how much would y be expected to change, holding all else constant? But reverse causal questions are important too. They’re a natural way to think (consider the importance of the word “Why”) and are arguably more important than forward questions.
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Introduction: Martin Lindquist and Michael Sobel published a fun little article in Neuroimage on models and assumptions for causal inference with intermediate outcomes. As their subtitle indicates (“A response to the comments on our comment”), this is a topic of some controversy. Lindquist and Sobel write: Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. . . . [Judea] Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. [C
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