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 […]

**Causal Inference**category.

## 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 […]

## 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 […]

## Crowdstorming a dataset

Raphael Silberzahn writes: Brian Nosek, Eric Luis Uhlmann, Dan Martin, and I just launched a project through the Open Science Center we think you’ll find interesting. The basic idea is to “Crowdstorm a Dataset”. Multiple independent analysts are recruited to test the same hypothesis on the same data set in whatever manner they see as […]

## Big Data…Big Deal? Maybe, if Used with Caution.

This post is by David K. Park As we have witnessed, the term “big data” has been thrusted onto the zeitgeist in the past several years, however, when one pushes beyond the hype, there seems to be little substance there. We’ve always had “data” so what so unique about it this time? Yes, we recognize it’s […]

## Adjudicating between alternative interpretations of a statistical interaction?

Jacob Felson writes: Say we have a statistically significant interaction in non-experimental data between two continuous predictors, X and Z and it is unclear which variable is primarily a cause and which variable is primarily a moderator. One person might find it more plausible to think of X as a cause and Z as a […]

## References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling

A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could […]