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Archive of posts filed under the Causal Inference category.

Kalesan, Fagan, and Galea respond to criticism of their paper on gun laws and deaths

The other day we posted some remarks on a recent paper by Bindu Kalesan, Jeffrey Fagan, Sandro Galea, “Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study.” In response to the criticisms from me and various commenters, the authors of the paper prepared a detailed response, which I’m linking to here. They […]

“Why this gun control study might be too good to be true”

Jeff Lax points us to this news article by Carolyn Johnson discussing a research paper, “Firearm legislation and firearm mortality in the USA: a cross-sectional, state-level study,” by Bindu Kalesan, Matthew Mobily, Olivia Keiser, Jeffrey Fagan, and Sandro Galea, that just appeared in the medical journal The Lancet. Here are the findings from Kalesan et […]

Fundamental difficulty of inference for a ratio when the denominator could be positive or negative

I happened to come across this post from 2011, which in turn is based on thoughts of mine from about 1993. It’s important and most of you probably haven’t seen it, so here it is again: Ratio estimates are common in statistics. In survey sampling, the ratio estimate is when you use y/x to estimate […]

Postdoc opportunity with Sophia Rabe-Hesketh and me in Berkeley!

Sophia writes: Mark Wilson, Zach Pardos and I are looking for a postdoc to work with us on a range of projects related to educational assessment and statistical modeling, such as Bayesian modeling in Stan (joint with Andrew Gelman). See here for more details. We will accept applications until February 26. The position is for […]

Why are trolls so bothersome?

We don’t get a lot of trolls on this blog. When people try, I typically respond with some mixture of directness and firmness, and the trolls either give up or perhaps they recognize that I am answering questions in sincerity, which does not serve their trollish purposes. But I’m pretty sure that my feeling is […]

The PACE trial and the problems with discrete, yes/no thinking

I don’t often read the Iranian Journal of Cancer Prevention, but I like this quote: I was thinking more about the PACE trial. God is in every leaf of every tree. There’s been a lot of discussion about statistical problems with the PACE papers, and also about the research team’s depressing refusal to share their […]

Hierarchical modeling when you have only 2 groups: I still think it’s a good idea, you just need an informative prior on the group-level variation

Dan Chamberlain writes: I am working on a Bayesian analysis of some data from a randomized controlled trial comparing two different drugs for treating seizures in children. I have been using your book as a resource and I have a question about hierarchical modeling. If you have the time, I would greatly appreciate any advice […]

Judea Pearl and I briefly discuss extrapolation, causal inference, and hierarchical modeling

OK, I guess it looks like the Buzzfeed-style headlines are officially over. Anyway, Judea Pearl writes: I missed the discussion you had here about Econometrics: Instrument locally, extrapolate globally, which also touched on my work with Elias Bareinboim. So, please allow me to start a new discussion about extrapolation and external validity. First, two recent […]

Econometrics: Instrument locally, extrapolate globally

Rajeev Dehejia sends along two papers, one with James Bisbee, Cristian Pop-Eleches, and Cyrus Samii on extrapolating estimated local average treatment effects to new settings, and one with Cristian Pop-Eleches and Cyrus Samii on external validity in natural experiments. This is important stuff, and they work it out in real examples.

3 postdoc opportunities you can’t miss—here in our group at Columbia! Apply NOW, don’t miss out!

Hey, just once, the Buzzfeed-style hype is appropriate. We have 3 amazing postdoc opportunities here, and you need to apply NOW. Here’s the deal: we’re working on some amazing projects. You know about Stan and associated exciting projects in computational statistics. There’s the virtual database query, which is the way I like to describe our […]

Mindset interventions are a scalable treatment for academic underachievement — or not?

Someone points me to this post by Scott Alexander, criticizing the work of psychology researcher Carol Dweck. Alexander looks carefully at an article, “Mindset Interventions Are A Scalable Treatment For Academic Underachievement,” by David Paunesku, Gregory Walton, Carissa Romero, Eric Smith, David Yeager, and Carol Dweck, and he finds the following: Among ordinary students, the […]

Have weak data. But need to make decision. What to do?

Vlad Malik writes: I just re-read your article “Of Beauty, Sex and Power”. In my line of work (online analytics), low power is a recurring, existential problem. Do we act on this data or not? If not, why are we even in this business? That’s our daily struggle. Low power seems to create a sort […]

Comments on Imbens and Rubin causal inference book

Guido Imbens and Don Rubin recently came out with a book on causal inference. The book’s great (of course I would say that, as I’ve collaborated with both authors) and it’s so popular that I keep having to get new copies because people keep borrowing my copy and not returning it. Imbens and Rubin come […]

Reprint of “Observational Studies” by William Cochran followed by comments by current researchers in observational studies

Dylan Small organized this discussion in the new journal, Observational Studies. Cochran’s 1972 article is followed by comments from: Norman Breslow Thomas Cook David Cox & Nanny Wermuth Stephen Fienberg Joseph Gastwirth & Barry Graubard Andrew Gelman Ben Hansen & Adam Sales Miguel Hernan Jennifer Hill Judea Pearl Paul Rosenbaum Donald Rubin Herbert Smith Mark […]

Evaluating the Millennium Villages Project

I’m a postdoc working with Andy and Jeff Sachs on the evaluation of the Millennium Villages Project, a ten-year economic development project operating in ten sub-Saharan African countries. Our evaluation protocol was recently accepted by The Lancet (full text here, and the accompanying technical paper here). We welcome your thoughts!

Michael LaCour in 20 years

In case you were wondering what “Bruno” Lacour will be doing a couple decades from now . . . James Delaney pointed me to this CNN news article, “Connecticut’s strict gun law linked to large homicide drop” by Carina Storrs: The rate of gun-related murders fell sharply in the 10 years after Connecticut implemented a […]

What to do to train to apply statistical models to political science and public policy issues

Taylor Good writes: I am a graduate of a state school with a BS in Math and a BA in Political Science, and I was wondering if you could give me some career advice. Knowing how you got to where you are now, what path would you advise someone to take to get to where […]

My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

What I got wrong (and right) about econometrics and unbiasedness

Yesterday I spoke at the Princeton economics department. The title of my talk was: “Unbiasedness”: You keep using that word. I do not think it means what you think it means. The talk went all right—people seemed ok with what I was saying—but I didn’t see a lot of audience involvement. It was a bit […]

A causal-inference version of a statistics problem: If you fit a regression model with interactions, and the underlying process has an interaction, your coefficients won’t be directly interpretable

A colleague pointed me to a recent paper, “Does Regression Produce Representative Estimates of Causal Effects?” by Peter Aronow and Cyrus Samii, which begins: With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation […]