andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1888 knowledge-graph by maker-knowledge-mining
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Introduction: Pearl reports that his Journal of Causal Inference has just posted its first issue , which contains a mix of theoretical and applied papers. Pearl writes that they welcome submissions on all aspects of causal inference.
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same-blog 1 1.0 1888 andrew gelman stats-2013-06-08-New Judea Pearl journal of causal inference
Introduction: Pearl reports that his Journal of Causal Inference has just posted its first issue , which contains a mix of theoretical and applied papers. Pearl writes that they welcome submissions on all aspects of causal inference.
2 0.43639559 879 andrew gelman stats-2011-08-29-New journal on causal inference
Introduction: Judea Pearl is starting an (online) Journal of Causal Inference. The first issue is planned for Fall 2011 and the website is now open for submissions. Here’s the background (from Pearl): Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression, simultaneous equations or logical implications, and glossing over the special ingredients needed for causal analysis. In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. In addition to significant original research articles, Journal of Causal Inference also welcomes: 1) Submissions that synthesize and assess cross-disciplinary methodological research 2) Submissions that discuss the history of the causal inference field and its philosophical underpinnings 3) Unsolicited short communi
Introduction: This material should be familiar to many of you but could be helpful to newcomers. Pearl writes: ALL causal conclusions in nonexperimental settings must be based on untested, judgmental assumptions that investigators are prepared to defend on scientific grounds. . . . To understand what the world should be like for a given procedure to work is of no lesser scientific value than seeking evidence for how the world works . . . Assumptions are self-destructive in their honesty. The more explicit the assumption, the more criticism it invites . . . causal diagrams invite the harshest criticism because they make assumptions more explicit and more transparent than other representation schemes. As regular readers know (for example, search this blog for “Pearl”), I have not got much out of the causal-diagrams approach myself, but in general I think that when there are multiple, mathematically equivalent methods of getting the same answer, we tend to go with the framework we are used
4 0.28039175 1624 andrew gelman stats-2012-12-15-New prize on causality in statstistics education
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
5 0.24798152 1133 andrew gelman stats-2012-01-21-Judea Pearl on why he is “only a half-Bayesian”
Introduction: In an article published in 2001, Pearl wrote: I [Pearl] turned Bayesian in 1971, as soon as I began reading Savage’s monograph The Foundations of Statistical Inference [Savage, 1962]. The arguments were unassailable: (i) It is plain silly to ignore what we know, (ii) It is natural and useful to cast what we know in the language of probabilities, and (iii) If our subjective probabilities are erroneous, their impact will get washed out in due time, as the number of observations increases. Thirty years later, I [Pearl] am still a devout Bayesian in the sense of (i), but I now doubt the wisdom of (ii) and I know that, in general, (iii) is false. He elaborates: The bulk of human knowledge is organized around causal, not probabilistic relationships, and the grammar of probability calculus is insufficient for capturing those relationships. Specifically, the building blocks of our scientific and everyday knowledge are elementary facts such as “mud does not cause rain” and “symptom
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same-blog 1 0.99652559 1888 andrew gelman stats-2013-06-08-New Judea Pearl journal of causal inference
Introduction: Pearl reports that his Journal of Causal Inference has just posted its first issue , which contains a mix of theoretical and applied papers. Pearl writes that they welcome submissions on all aspects of causal inference.
2 0.91693872 879 andrew gelman stats-2011-08-29-New journal on causal inference
Introduction: Judea Pearl is starting an (online) Journal of Causal Inference. The first issue is planned for Fall 2011 and the website is now open for submissions. Here’s the background (from Pearl): Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression, simultaneous equations or logical implications, and glossing over the special ingredients needed for causal analysis. In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. In addition to significant original research articles, Journal of Causal Inference also welcomes: 1) Submissions that synthesize and assess cross-disciplinary methodological research 2) Submissions that discuss the history of the causal inference field and its philosophical underpinnings 3) Unsolicited short communi
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Introduction: In the spirit of Dehejia and Wahba: Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons , by Cook, Shadish, and Wong. Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments, by Shadish, Clark, and Steiner. I just talk about causal inference. These people do it. The second link above is particularly interesting because it includes discussions by some causal inference heavyweights. WWJD and all that.
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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: 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: Pearl reports that his Journal of Causal Inference has just posted its first issue , which contains a mix of theoretical and applied papers. Pearl writes that they welcome submissions on all aspects of causal inference.
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Introduction: I’m on an island in Maine for a few weeks (big shout out for North Haven!) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers.” I think it’s great that a small coastal newspaper carries reporting like this. (The online version has a few things that I don’t recall in the print version, too, so it’s even better). But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. “We included the inshore trawl data from Maine and other state surveys, in addition to federal survey data; we h
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Introduction: U.S. Treasury, Office of Financial Research, Tues 9 Apr afternoon (I don’t actually know exactly when or in what room): Parameterization and Bayesian Modeling — Johns Hopkins University, Department of Biostatistics, 4pm Wed 10 Apr, room W2030 School of Public Health : Little data: How traditional statistical ideas remain relevant in a big-data world At the end of the day, after all the processing, big data are being used to answer little- data questions such as, Does an observed pattern generalize to the larger population?, or Could it be explained by alternative processes (sometimes called “chance”)? We discuss some recent ideas in the world of “little data” that remain of big importance.
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Introduction: A colleague writes: When I was in NYC I went to this party by group of Japanese bio-scientists. There, one guy told me about how the biggest pharmaceutical company in Japan did their statistics. They ran 100 different tests and reported the most significant one. (This was in 2006 and he said they stopped doing this few years back so they were doing this until pretty recently…) I’m not sure if this was 100 multiple comparison or 100 different kinds of test but I’m sure they wouldn’t want to disclose their data… Ouch!
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Introduction: Mark Johnstone writes: I’ve recently been investigating a new European Court of Justice ruling on insurance calculations (on behalf of MoneySuperMarket) and I found something related to statistics that caught my attention. . . . The ruling (which comes into effect in December 2012) states that insurers in Europe can no longer provide different premiums based on gender. Despite the fact that women are statistically safer drivers, unless it’s biologically proven there is a causal relationship between being female and being a safer driver, this is now seen as an act of discrimination (more on this from the Wall Street Journal). However, where do you stop with this? What about age? What about other factors? And what does this mean for the application of statistics in general? Is it inherently unjust in this context? One proposal has been to fit ‘black boxes’ into cars so more individual data can be collected, as opposed to relying heavily on aggregates. For fans of data and s
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