Contamination Bias in Linear Regressions

Paul Goldsmith-Pinkham, Peter Hull and Michal Kolesár

American Economic Review, 2024, vol. 114, issue 12, 4015-51

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 nonconvex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A reanalysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores.

JEL-codes: C21 C31 C51 H75 I21 I28 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Contamination Bias in Linear Regressions (2024)
Working Paper: Contamination Bias in Linear Regressions (2022)
Working Paper: Contamination Bias in Linear Regressions (2022)
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DOI: 10.1257/aer.20221116

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