Contamination Bias in Linear Regressions

Paul Goldsmith-Pinkham, Peter Hull and Michal Kolesár

No 30108, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that 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 the targeting of easiest-to-estimate weighted average effects. A re-analysis 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: C14 C21 C22 C90 (search for similar items in EconPapers)
Date: 2022-06
New Economics Papers: this item is included in nep-exp
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Related works:
Journal Article: Contamination Bias in Linear Regressions (2024)
Working Paper: Contamination Bias in Linear Regressions (2024)
Working Paper: Contamination Bias in Linear Regressions (2022)
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