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Contamination Bias in Linear Regressions
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.
Date:
11 October 2022, 14:00
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Seminar Room B
Speaker:
Michal Kolesar (Princeton University)
Organising department:
Department of Economics
Part of:
Econometrics Lunch
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Melis Clark
