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Archive of posts filed under the Causal Inference category.

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 http://blog.revolutionanalytics.com/2014/09/google-uses-r-to-calculate-roi-on-advertising-campaigns.html? 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 […]

Estimating discontinuity in slope of a response function

Peter Ganong sends me a new paper (coauthored with Simon Jager) on the “regression kink design.” Ganong writes: The method is a close cousin of regression discontinuity and has gotten a lot of traction recently among economists, with over 20 papers in the past few years, though less among statisticians. We propose a simple placebo […]

Estimated effect of early childhood intervention downgraded from 42% to 25%

Last year I came across an article, “Labor Market Returns to Early Childhood Stimulation: a 20-year Followup to an Experimental Intervention in Jamaica,” by Paul Gertler, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor, that claimed that early childhood stimulation raised adult earnings by 42%. At the […]

The health policy innovation center: how best to move from pilot studies to large-scale practice?

A colleague pointed me to this news article regarding evaluation of new health plans: The Affordable Care Act would fund a new research outfit evocatively named the Innovation Center to discover how to most effectively deliver health care, with $10 billion to spend over a decade. But now that the center has gotten started, many […]

A linguist has a question about sampling when the goal is causal inference from observational data

Nate Delaney-Busch writes: I’m a PhD student of cognitive neuroscience at Tufts, and a question came recently with my colleagues about the difficulty of random sampling in cases of highly controlled stimulus sets, and I thought I would drop a line to see if you had any reading suggestions for us. Let’s say I wanted […]

“The Europeans and Australians were too eager to believe in renal denervation”

As you can see, I’m having a competition with myself for the most boring title ever. The story, though, is not boring. Paul Alper writes: I just came across this in the NYT. Here is the NEJM article itself: And here is the editorial in the NEJM: The gist is that on the basis of […]

More on those randomistas

Following up on our recent post, I clicked on some of Ziliak’s links and found lots of good stuff, especially the post by Berk Ozler. I have no knowledge of his work but I like his writing; see here, for example. Ziliak replied: Ozler’s post is very good indeed, and well written. Ozler’s suggestion for […]

Smullyan and the Randomistas

Steve Ziliak wrote in: I thought you might be interested in the following exchanges on randomized trials: Here are a few exchanges on the economics and ethics of randomized controlled trials, reacting to my [Zilliak’s] study with Edward R. Teather-Posadas, “The Unprincipled Randomization Principle in Economics and Medicine”. Our study is forthcoming in the Oxford […]

It’s not matching or regression, it’s matching and regression.

A colleague writes: Why do people keep praising matching over regression for being non parametric? Isn’t it f’ing parametric in the matching stage, in effect, given how many types of matching there are… you’re making structural assumptions about how to deal with similarities and differences…. the likelihood two observations are similar based on something quite […]