andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-307 knowledge-graph by maker-knowledge-mining
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Introduction: John Christie sends along this . As someone who owns neither a car nor a mobile phone, it’s hard for me to relate to this one, but it’s certainly a classic example for teaching causal inference.
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same-blog 1 1.0 307 andrew gelman stats-2010-09-29-“Texting bans don’t reduce crashes; effects are slight crash increases”
Introduction: John Christie sends along this . As someone who owns neither a car nor a mobile phone, it’s hard for me to relate to this one, but it’s certainly a classic example for teaching causal inference.
2 0.20440009 1065 andrew gelman stats-2011-12-17-Read this blog on Google Currents
Introduction: I’ve been told that if you download Google Currents to your iPad or Android device, you get a blog reader that beautifies the posts and makes it look more like a magazine. I don’t have a mobile phone myself but maybe those of you who do, will find this useful.
3 0.17952743 48 andrew gelman stats-2010-05-23-The bane of many causes
Introduction: One of the newsflies buzzing around today is an article “Brain tumour risk in relation to mobile telephone use: results of the INTERPHONE international case-control study” . The results, shown in this pretty table below, appear to be inconclusive. A limited amount of cellphone radiation is good for your brain, but not too much? It’s unfortunate that the extremes are truncated. The commentary at Microwave News blames bias: The problem with selection bias –also called participation bias– became apparent after the brain tumor risks observed throughout the study were so low as to defy reason. If they reflect reality, they would indicate that cell phones confer immediate protection against tumors. All sides agree that this is extremely unlikely. Further analysis pointed to unanticipated differences between the cases (those with brain tumors) and the controls (the reference group). The second problem concerns how accurately study participants could recall the amount of t
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Introduction: This entry was posted by Phil Price. A colleague is looking at data on car (and SUV and light truck) collisions and casualties. He’s interested in causal relationships. For instance, suppose car manufacturers try to improve gas mileage without decreasing acceleration. The most likely way they will do that is to make cars lighter. But perhaps lighter cars are more dangerous; how many more people will die for each mpg increase in gas mileage? There are a few different data sources, all of them seriously deficient from the standpoint of answering this question. Deaths are very well reported, so if someone dies in an auto accident you can find out what kind of car they were in, what other kinds of cars (if any) were involved in the accident, whether the person was a driver or passenger, and so on. But it’s hard to normalize: OK, I know that N people who were passengers in a particular model of car died in car accidents last year, but I don’t know how many passenger-miles that
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Introduction: Xian sends along this link that might be of interest to some of you.
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same-blog 1 0.98416013 307 andrew gelman stats-2010-09-29-“Texting bans don’t reduce crashes; effects are slight crash increases”
Introduction: John Christie sends along this . As someone who owns neither a car nor a mobile phone, it’s hard for me to relate to this one, but it’s certainly a classic example for teaching causal inference.
2 0.72822016 340 andrew gelman stats-2010-10-13-Randomized experiments, non-randomized experiments, and observational studies
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.
3 0.69289255 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.
4 0.68701679 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 0.62346017 1675 andrew gelman stats-2013-01-15-“10 Things You Need to Know About Causal Effects”
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: What are the x and y-axes here ? P.S. Popeye nails it (see comments).
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Introduction: John Christie sends along this . As someone who owns neither a car nor a mobile phone, it’s hard for me to relate to this one, but it’s certainly a classic example for teaching causal inference.
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Introduction: See here for latest rant.
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Introduction: In his review in 1938 of Historical Development of the Graphical Representation of Statistical Data , by H. Gray Funkhauser, for The Economic Journal , the great economist writes: Perhaps the most striking outcome of Mr. Funkhouser’s researches is the fact of the very slow progress which graphical methods made until quite recently. . . . In the first fifty volumes of the Statistical Journal, 1837-87, only fourteen graphs are printed altogether. It is surprising to be told that Laplace never drew a graph of the normal law of error . . . Edgeworth made no use of statistical charts as distinct from mathematical diagrams. Apart from Quetelet and Jevons, the most important influences were probably those of Galton and of Mulhall’s Dictionary, first published in 1884. Galton was indeed following his father and grandfather in this field, but his pioneer work was mainly restricted to meteorological maps, and he did not contribute to the development of the graphical representation of ec
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Introduction: Geen Tomko asks: Can you recommend a good introductory book for statistical computation? Mostly, something that would help make it easier in collecting and analyzing data from student test scores. I don’t know. Usually, when people ask for a starter statistics book, my recommendation (beyond my own books) is The Statistical Sleuth. But that’s not really a computation book. ARM isn’t really a statistical computation book either. But the statistical computation books that I’ve seen don’t seems so relevant for the analyses that Tomko is looking for. For example, the R book of Venables and Ripley focuses on nonparametric statistics, which is fine but seems a bit esoteric for these purposes. Does anyone have any suggestions?
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