andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1778 knowledge-graph by maker-knowledge-mining
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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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same-blog 1 1.0 1778 andrew gelman stats-2013-03-27-My talk at the University of Michigan today 4pm
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 .
2 0.49903372 2207 andrew gelman stats-2014-02-11-My talks in Bristol this Wed and London this Thurs
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
3 0.25557163 785 andrew gelman stats-2011-07-02-Experimental reasoning in social science
Introduction: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when you change something, it is necessary to change it.” At the same time, in my capacity as a social scientist, I’ve published many applied research papers, almost none of which have used experimental data. In the present article, I’ll address the following questions: 1. Why do I agree with the consensus characterization of randomized experimentation as a gold standard? 2. Given point 1 above, why does almost all my research use observational data? In confronting these issues, we must consider some general issues in the strategy of social science research. We also take from the psychology methods literature a more nuanced perspective that considers several differen
Introduction: That’s the title of my talk at Montana State University this Thursday (21 Mar). For those of you who happen to be in the area, it’s 3:30-5:00pm in the Procrastinator Theater. I’m also speaking in the statistics seminar from 11-12:15 in the Byker Auditorium in the Chemistry building. Topic: Causality and Statistical Learning . P.S. My title is a bit Geng-inspired, which reminds me that I recently came across this interesting mini-bio.
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: 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: 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: 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: 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: 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: 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 .
2 0.90218413 1127 andrew gelman stats-2012-01-18-The Fixie Bike Index
Introduction: Where are the fixed-gear bike riders? Rohin Dhar explains : At Priceonomics, in order to build our bicycle price guide, we measure what kind of used bikes people are trying to sell and the quantity sold in any city. By mining our database of 1.3 million bicycle listings, we can tell what are the largest markets for used bicycles, how the prices vary by region, and where people who prize fixed gear bikes live. Fixies (fixed gear bikes) are considered to be a strong indicator of hipsterness. For those unfamiliar, a fixed gear bike requires riding in a single gear and the only way to stop the bike is to pedal backwards to help skid the bike to a halt. You can’t “coast” on a fixie; when you are biking downhill, your pedals will keep moving so you better keep pedaling too. Because of the minimalism of this fixed gear system, the bikes tend to be aesthetically pleasing but somewhat challenging to ride. . . . In short, fixed gear bikes = hipsters, and New York boroug
3 0.88848114 2192 andrew gelman stats-2014-01-30-History is too important to be left to the history professors, Part 2
Introduction: Completely non-gay historian Niall Ferguson, a man who we can be sure would never be caught at a ballet or a poetry reading, informs us that the British decision to enter the first world war on the side of France and Belgium was “the biggest error in modern history.” Ummm, here are a few bigger errors: The German decision to invade Russia in 1941. The Japanese decision to attack America in 1941. Oh yeah , the German decision to invade Belgium in 1914. The Russian decision to invade Afghanistan in 1981 doesn’t look like such a great decision either. And it wasn’t so smart for Saddam Hussein to invade Kuwait, but maybe the countries involved were too small for this to count as “the biggest error in modern history.” It’s striking that, in considering the biggest error in modern history, Ferguson omits all these notorious acts of aggression (bombing Pearl Harbor, leading to the destruction of much of your country, that was pretty bad, huh?), and decides that the worst
4 0.86853743 992 andrew gelman stats-2011-11-05-Deadwood in the math curriculum
Introduction: Mark Palko asks : What are the worst examples of curriculum dead wood? Here’s the background: One of the first things that hit me [Palko] when I started teaching high school math was how much material there was to cover. . . . The most annoying part, though, was the number of topics that could easily have been cut, thus giving the students the time to master the important skills and concepts. The example that really stuck with me was synthetic division, a more concise but less intuitive way of performing polynomial long division. Both of these topics are pretty much useless in daily life but polynomial long division does, at least, give the student some insight into the relationship between polynomials and familiar base-ten numbers. Synthetic division has no such value; it’s just a faster but less interesting way of doing something you’ll never have to do. I started asking hardcore math people — mathematicians, statisticians, physicists, rocket scientists — if they.’d ever u
Introduction: Xian pointed me to this recycling of a classic probability error. It’s too bad it was in the New York Times, but at least it was in the Opinion Pages, so I guess that’s not so bad. And, on the plus side, several of the blog commenters got the point. What I was wondering, though, was who was this “Yitzhak Melechson, a statistics professor at the University of Tel Aviv”? This is such a standard problem, I’m surprised to find a statistics professor making this mistake. I was curious what his area of research is and where he was trained. I started by googling Yitzhak Melechson but all I could find was this news story, over and over and over and over again. Then I found Tel Aviv University and navigated to its statistics department but couldn’t find any Melechson in the faculty list. Next stop: entering Melechson in the search engine at the Tel Aviv University website. It came up blank. One last try: I entered the Yitzhak Melechson into Google Scholar. Here’s what came up:
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