andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-950 knowledge-graph by maker-knowledge-mining
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Introduction: Dave Backus writes: We macroeconomists are thrilled with the Nobel prize for Sargent and Sims. But on causality: they spent more time showing how hard it was to identify causality than showing how to do it. And that’s a fair assessment of our field [economics]: causality is almost always in doubt. More here . If I were in a snarky mood, I’d say something like, Causality is always in doubt in economics . . . unless you’re talking about abortion and crime, in which case you can be absolutely certain. But I’m in a good mood right now so I won’t say that. Instead I’ll just remark that, as a statistician, I’m positively thrilled that somebody named “Sims” received a major award.
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1 Dave Backus writes: We macroeconomists are thrilled with the Nobel prize for Sargent and Sims. [sent-1, score-0.622]
2 But on causality: they spent more time showing how hard it was to identify causality than showing how to do it. [sent-2, score-1.166]
3 And that’s a fair assessment of our field [economics]: causality is almost always in doubt. [sent-3, score-0.976]
4 If I were in a snarky mood, I’d say something like, Causality is always in doubt in economics . [sent-5, score-0.652]
5 unless you’re talking about abortion and crime, in which case you can be absolutely certain. [sent-8, score-0.502]
6 But I’m in a good mood right now so I won’t say that. [sent-9, score-0.465]
7 Instead I’ll just remark that, as a statistician, I’m positively thrilled that somebody named “Sims” received a major award. [sent-10, score-0.955]
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same-blog 1 1.0000001 950 andrew gelman stats-2011-10-10-“Causality is almost always in doubt”
Introduction: Dave Backus writes: We macroeconomists are thrilled with the Nobel prize for Sargent and Sims. But on causality: they spent more time showing how hard it was to identify causality than showing how to do it. And that’s a fair assessment of our field [economics]: causality is almost always in doubt. More here . If I were in a snarky mood, I’d say something like, Causality is always in doubt in economics . . . unless you’re talking about abortion and crime, in which case you can be absolutely certain. But I’m in a good mood right now so I won’t say that. Instead I’ll just remark that, as a statistician, I’m positively thrilled that somebody named “Sims” received a major award.
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Introduction: Causality and Statistical Learning Andrew Gelman, Statistics and Political Science, Columbia University Wed 27 Mar, 4pm, Betty Ford Auditorium, Ford School of Public Policy Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification yet must learn from available information. Two relevant papers are here and here .
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Introduction: Following up on my post responding to his question about that controversial claim that high genetic diversity, or low genetic diversity, is bad for the economy, Kyle Peyton writes: I’m happy to see you’ve articulated similar gripes I had w/ the piece, which makes me feel like I’m not crazy. I remember discussing this with colleagues (I work at a research institute w/ economists) and only a couple of them shared any concern. It seems that by virtue of being published in ‘the AER’ the results are unquestionable. I agree that the idea is interesting and worth pursuing but as you say it’s one thing to go from that to asserting ‘causality’ (I still don’t know what definition of causality they’re using?). All the data torture along the way is just tipping the hat to convention rather than serving any scientific purpose. Some researchers are so uptight about identification that, when they think they have it, all their skepticism dissolves. Even in a case like this where that causal tr
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Introduction: John Horton points to Sims ‘s comment on Angrist and Pischke : Top of page 8—he criticizes economists for using clustered standard errors—suggests using multilevel models instead. Awesome! So now there are at least two Nobel prize winners in economics who’ve expressed skepticism about controlled experiments. (I wonder if Sims is such a danger in a parking lot.) P.S. I’m still miffed that this journal didn’t invite me to comment on that article!
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Introduction: Judea Pearl writes: Can you post the announcement below on your blog? And, by all means, if you find heresy in my interview with Ron Wasserstein, feel free to criticize it with your readers. I responded that I’m not religious, so he’ll have to look for someone else if he’s looking for findings of heresy. I did, however, want to share his announcement: The American Statistical Association has announced a new Prize , “Causality in Statistics Education”, aimed to encourage the teaching of basic causal inference in introductory statistics courses. The motivations for the prize are discussed in an interview I [Pearl] gave to Ron Wasserstein. I hope readers of this list will participate, either by innovating new tools for teaching causation or by nominating candidates who deserve the prize. And speaking about education, Bryant and I [Pearl] have revised our survey of econometrics textbooks, and would love to hear your suggestions on how to restore causal inference to e
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Introduction: 1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol): Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification, yet must learn from available information. This is a good one. They laughed their asses off when I did it in Ann Arbor. But it has serious stuff too. As George Carlin (or, for that matter, John or Brad) might say, it’s funny because it’s true. Here are some old slides, but I plan to mix in a bit of new material. 2. Theoretical Statistics is the Theory of Applied Statistics
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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
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Introduction: Dave Backus writes: We macroeconomists are thrilled with the Nobel prize for Sargent and Sims. But on causality: they spent more time showing how hard it was to identify causality than showing how to do it. And that’s a fair assessment of our field [economics]: causality is almost always in doubt. More here . If I were in a snarky mood, I’d say something like, Causality is always in doubt in economics . . . unless you’re talking about abortion and crime, in which case you can be absolutely certain. But I’m in a good mood right now so I won’t say that. Instead I’ll just remark that, as a statistician, I’m positively thrilled that somebody named “Sims” received a major award.
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Introduction: Causality and Statistical Learning Andrew Gelman, Statistics and Political Science, Columbia University Wed 27 Mar, 4pm, Betty Ford Auditorium, Ford School of Public Policy Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification yet must learn from available information. Two relevant papers are here and here .
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Introduction: Pointing to some horrible graphs, Kaiser writes, “The Earth Institute needs a graphics adviser.” I agree. The graphs are corporate standard, neither pretty or innovative enough to qualify as infographics, not informational enough to be good statistical data displays. Some examples include the above exploding pie chart, which, as Kaiser notes, is not merely ugly and ridiculously difficult to read (given that it is conveying only nine data points) but also invites suspicion of its numbers, and pages and pages of graphs that could be better compressed into a compact displays (see pages 25-65 of the report). Yes, this is all better than tables of numbers, but I don’t see that much thought went into displaying patterns of information or telling a story. It’s more graph-as-data-dump. To be fair, the report does have some a clean scatterplot (on page 65). But, overall, the graphs are not well-integrated with the messages in the text. I feel a little bit bad about this, beca
Introduction: Erin Jonaitis points us to this article by Christopher Ferguson and Moritz Heene, who write: Publication bias remains a controversial issue in psychological science. . . . that the field often constructs arguments to block the publication and interpretation of null results and that null results may be further extinguished through questionable researcher practices. Given that science is dependent on the process of falsification, we argue that these problems reduce psychological science’s capability to have a proper mechanism for theory falsification, thus resulting in the promulgation of numerous “undead” theories that are ideologically popular but have little basis in fact. They mention the infamous Daryl Bem article. It is pretty much only because Bem’s claims are (presumably) false that they got published in a major research journal. Had the claims been true—that is, had Bem run identical experiments, analyzed his data more carefully and objectively, and reported that the r
Introduction: I received the following in email from our publisher: I write with regards to the project to publish a China Edition of your book “Data Analysis Using Regression and Multilevel/Hierarchical Models” (ISBN-13: 9780521686891) for the mainland Chinese market. I regret to inform you that we have been notified by our partner in China, Posts & Telecommunications Press (PTP), that due to various politically sensitive materials in the text, the China Edition has not met with the approval of the publishing authorities in China, and as such PTP will not be able to proceed with the publication of this edition. We will therefore have to cancel plans for the China Edition of your book. Please accept my apologies for this unforeseen development. If you have any queries regarding this, do feel free to let me know. Oooh, it makes me feel so . . . subversive. It reminds me how, in Sunday school, they told us that if we were ever visiting Russia, we should smuggle Bibles in our luggage because the
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