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Archive of posts filed under the Causal Inference category.

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 […]

Regression and causality and variable ordering

Bill Harris wrote in with a question: David Hogg points out in one of his general articles on data modeling that regression assumptions require one to put the variable with the highest variance in the ‘y’ position and the variable you know best (lowest variance) in the ‘x’ position. As he points out, others speak […]

A whole fleet of gremlins: Looking more carefully at Richard Tol’s twice-corrected paper, “The Economic Effects of Climate Change”

We had a discussion the other day of a paper, “The Economic Effects of Climate Change,” by economist Richard Tol. The paper came to my attention after I saw a notice from Adam Marcus that it was recently revised because of data errors. But after looking at the paper more carefully, I see a bunch […]

How much can we learn about individual-level causal claims from state-level correlations?

Hey, we all know the answer: “correlation does not imply causation”—but of course life is more complicated than that. As philosophers, economists, statisticians, and others have repeatedly noted, most of our information about the world is observational not experimental, yet we manage to draw causal conclusions all the time. Sure, some of these conclusions are […]

Seth Roberts

I met Seth back in the early 1990s when we were both professors at the University of California. He sometimes came to the statistics department seminar and we got to talking about various things; in particular we shared an interest in statistical graphics. Much of my work in this direction eventually went toward the use […]