Category: Causal Inference

Difference-in-difference estimators are a special case of lagged regression

Fan Li and Peng Ding write: Difference-in-differences is a widely-used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trend, which is scale dependent and may be questionable in some applications. A common alternative method is a regression model that adjusts for the lagged dependent […]

Continuing discussion of status threat and presidential elections, with discussion of challenge of causal inference from survey data

Last year we reported on an article by sociologist Steve Morgan, criticizing a published paper by political scientist Diana Mutz. A couple months later we updated with Mutz’s response to Morgan’s critique. Finally, Morgan has published a reply to Mutz’s response to Morgan’s comments on Mutz’s paper. Here’s a passage that is of methodological interest: […]

Automatic voter registration impact on state voter registration

Sean McElwee points us to this study by Kevin Morris and Peter Dunphy, who write: Automatic voter registration or AVR . . . features two seemingly small but transformative changes to how people register to vote: 1. Citizens who interact with government agencies like the Department of Motor Vehicles are registered to vote, unless they […]

Wanted: Statistical success stories

Bill Harris writes: Sometime when you get a free moment, it might be great to publish a post that links to good, current exemplars of analyses. There’s a current discussion about RCTs on a program evaluation mailing list I monitor. I posted links to your power=0.06 post and your Type S and Type M post, […]

Active learning and decision making with varying treatment effects!

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write: Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target […]

What sort of identification do you get from panel data if effects are long-term? Air pollution and cognition example.

Don MacLeod writes: Perhaps you know this study which is being taken at face value in all the secondary reports: “Air pollution causes ‘huge’ reduction in intelligence, study reveals.” It’s surely alarming, but the reported effect of air pollution seems implausibly large, so it’s hard to be convinced of it by a correlational study alone, […]

“Heckman curve” update: The data don’t seem to support the claim that human capital investments are most effective when targeted at younger ages.

David Rea and Tony Burton write: The Heckman Curve describes the rate of return to public investments in human capital for the disadvantaged as rapidly diminishing with age. Investments early in the life course are characterised as providing significantly higher rates of return compared to investments targeted at young people and adults. This paper uses […]

Treatment interactions can be hard to estimate from data.

Brendan Nyhan writes: Per #3 here, just want to make sure you saw the Coppock Leeper Mullinix paper indicating treatment effect heterogeneity is rare. My reply: I guess it depends on what is being studied. In the world of evolutionary psychology etc., interactions are typically claimed to be larger than main effects (for example, that […]

Postdoc in Chicago on statistical methods for evidence-based policy

Beth Tipton writes: The Institute for Policy Research and the Department of Statistics is seeking applicants for a Postdoctoral Fellowship with Dr. Larry Hedges and Dr. Elizabeth Tipton. This fellowship will be a part of a new center which focuses on the development of statistical methods for evidence-based policy. This includes research on methods for […]