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Home | Events Archive | Contamination Bias in Linear Regressions

Contamination Bias in Linear Regressions

  • Location
    University of Amsterdam, Room E5.22 Amsterdam
  • Date and time

    March 10, 2023
    12:30 - 13:30


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 and Peter Hull

Link to article