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Archive of posts filed under the Causal Inference category.

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 […]

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 […]