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Archive of posts filed under the Causal Inference category.

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 […]

This year’s Atlantic Causal Inference Conference: 20-21 May

Dylan Small writes: The conference will take place May 20-21 (with a short course on May 19th) and the web site for the conference is here. The deadline for submitting a poster title for the poster session is this Friday. Junior researchers (graduate students, postdoctoral fellows, and assistant professors) whose poster demonstrates exceptional research will […]

“Thinking about the possibility of spurious correlation isn’t a matter of liking—it should be pretty much automatic.”

I agree with sociologist David Weakliem when he writes the above sentence. Here’s the full paragraph: Krugman says, “you can, if you like, try to argue that this relationship is spurious, maybe not causal.” Actually, I [Weakliem] liked his original figure, since I agree with Krugman on economic policy. But thinking about the possibility of […]

Time-release pedagogy??

Mark Palko points to this report and writes: Putting aside my concerns with the “additional years of learning” metric (and I have a lot of them), I have the feeling that there’s something strange here or i’m missing something obvious. That jump from 3-year impact to 4-year seems excessive. The press release links to a […]

Bayesian models, causal inference, and time-varying exposures

Mollie Wood writes: I am a doctoral student in clinical and population health research. My dissertation research is on prenatal medication exposure and neurodevelopmental outcomes in children, and I’ve encountered a difficult problem that I hope you might be able to advise me on. I am working on a problem in which my main exposure […]

The State of the Art in Causal Inference: Some Changes Since 1972

For the first issue of the journal Observational Studies, editor Dylan Small will reprint William Cochran’s 1972 article on the topic (which begins, “Observational studies are a class of statistical studies that have increased in frequency and importance during the past 20 years. In an observational study the investigator is restricted to taking selected observations […]