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Archive of posts filed under the Causal Inference category.

Stock-and-flow and other concepts that are important in statistical modeling but typically don’t get taught to statisticians

Bill Harris writes: You’ve written about causality somewhat often, and you, along with perhaps everyone who has done anything with statistics, have written that “correlation is not causation.” When you say that correlation is not causation, you seem to be pointing out cases where correlation exists but causality does not. While that’s important, there’s another […]

Causal Impact from Google

Bill Harris writes: Did you see Would that be something worth a joint post and discussion from you and Judea? I then wrote: Interesting. It seems to all depend on the choice of “control time series.” That said, it could still be a useful method. Bill replied: The good: Bayesian approaches made very approachable […]

Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]

“Epidemiology and Biostatistics: competitive or complementary?”

Mohammad Mansournia writes: I have a 20 minute lecture on “Epidemiology and Biostatistics: competitive or complementary?” at Tehran University of Medical Sciences in the next month. I should mention the difference between an epidemiologist and a biostatistician and their competitive or complementary roles in public health. I am wondering if you have any thoughts on […]

Designing a study to see if “the 10x programmer” is a real thing

Lorin H. writes: One big question in the world of software engineering is: how much variation is there in productivity across programmers? (If you google for “10x programmer” you’ll see lots of hits). Let’s say I wanted to explore this research question with a simple study. Choose a set of participants at random from a […]

If observational studies are outlawed, then only outlaws will do observational studies

My article “Experimental reasoning in social science” begins as follows: 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 […]

Retrospective clinical trials?

Kelvin Leshabari writes: I am a young medical doctor in Africa who wondered if it is possible to have a retrospective designed randomised clinical trial and yet be sound valid in statistical sense. This is because to the best of my knowledge, the assumptions underlying RCT methodology include that data is obtained in a prospective […]

The history of MRP highlights some differences between political science and epidemiology

Responding to a comment from Thomas Lumley (who asked why MRP estimates often seem to appear without any standard errors), I wrote: In political science, MRP always seems accompanied by uncertainty estimates. However, when lots of things are being displayed at once, it’s not always easy to show uncertainty, and in many cases I simply […]

I love it when I can respond to a question with a single link

Shira writes: This came up from trying to help a colleague of mine at Human Rights Watch. He has several completely observed variables X, and a variable with 29% missing, Y. He wants a histogram (and other descriptive statistics) of a “filled in” Y. He can regress Y on X, and impute missing Y’s from […]

Why I’m still not persuaded by the claim that subliminal smiley-faces can have big effects on political attitudes

We had a discussion last month on the sister blog regarding the effects of subliminal messages on political attitudes.  It started with a Larry Bartels post entitled “Here’s how a cartoon smiley face punched a big hole in democratic theory,” with the subtitle, “Fleeting exposure to ‘irrelevant stimuli’ powerfully shapes our assessments of policy arguments,” discussing the […]