Skip to content
Archive of posts filed under the Causal Inference category.

When doing causal inference, define your treatment decision and then consider the consequences that flow from it

Danielle Fumia writes: I am a research at the Washington State Institute for Public Policy, and I work on research estimating the effect of college attendance on earnings. Many studies that examine the effect of attending college on earnings control for college degree receipt and work experience. These models seem to violate the practice you […]

Bias against women in academia

I’m not the best one to write about this: to the extent that there’s bias in favor of men, I’ve been a beneficiary. Also I’m not familiar with the research on the topic. I know there are some statistical difficulties in setting up these causal questions, comparable to the difficulties arising in using “hedonic regression” […]

The Access to Justice Lab at Harvard Law School: Job Openings!

Jim Greiner writes: The Access to Justice Lab is a startup effort, initially supported by the Laura and John Arnold Foundation with sufficient funds for three years, headed by Jim Greiner at Harvard Law School. The Lab will produce randomized control trials (“RCTs”) directly involving courts and lawyers, particularly in the areas of access to […]

The causal inference competition you’ve all been waiting for!

Jennifer Hill announces “the first-ever ACIC causal inference data analysis competition”: Is your SATT where it’s at? Participate by submitting treatment effect estimates across a range of datasets OR by submitting a function (in any of a variety of programming languages) that will take input (covariate, treatment assignment, and response) and generate a treatment effect […]

A short answer to a short question

Emir Efendic writes: What is your opinion and can you think of any critiques of the multiple mediation models by Preacher and Hayes (e.g. Preacher & Hayes, 2008)? What would be your method of choice if you were testing multiple possible mediators of an effect, but also if said mediators are connected in a model […]

2016 Atlantic Causal Inference Conference

Jennifer Hill writes: Registration for the 2016 Atlantic Causal Inference Conference is now live. Stay tuned for short course registration (free for conference participants) and an announcement regarding a causal inference data analysis competition…both coming soon! Also please consider signing up to give a lightning talk (link on website). The conference will be held 26-27 […]

Postdoc in Alabama on obesity-related research using statistics

David Allison writes:

In the biggest advance in applied mathematics since the most recent theorem that Stephen Wolfram paid for . . .

Seth Green writes: I thought you might enjoy this update from the STATA team: . . . suppose we wish to know the effect on employment status of a job training program. Further suppose that motivation affects employment status and motivation affects participation. We do not observe motivation. We have an endogeneity problem. Stata 14’s […]

Numbers too good to be true? Or: Thanks, Obama!?

This post is by Phil. The “Affordable Care Act” a.k.a. “Obamacare” was passed in 2010, with its various pieces coming into play over the following few years. One of those pieces is penalties for hospitals that see high readmission rates. The theory here, or at least one of the theories here, was that hospitals could […]

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 […]