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Peter HULL (Brown University) – « Contamination Bias in Linear Regressions »

March 28 @ 12:15 pm - 1:30 pm

Applied Micro Seminar : Every Tuesday
Time: 12:15 pm – 13:30 pm
Date: 28th of March
Room : 3001

Peter HULL (Brown University) – « Contamination Bias in Linear Regressions »

Abstract :

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show these regressions generally fail to estimate convex averages of heterogeneous treatment effects; instead, estimates of each treatment’s effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including a new estimator of efficiently weighted average effects. We find minimal bias in a re-analysis of Project STAR, due to idiosyncratic effect heterogeneity. But sizeable contamination bias arises when effect heterogeneity becomes correlated with treatment propensity scores.


Joint work with : Paul Goldsmith-Pinkham (Yale University), Michal Kolesár (Princeton University)


Benoît SCHMUTZ (Pôle d’économie du CREST)
Roland RATHELOT (Pôle d’économie du CREST)