Contamination Bias in Linear Regressions

Paul Goldsmith-Pinkham, Peter Hull and Michal Kolesár
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Michal Kolesár: Princeton University

Working Papers from Princeton University. Economics Department.

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.

Keywords: Bias; Decision making; Contamination; Heterogeneity (search for similar items in EconPapers)
JEL-codes: C14 C21 C22 C90 (search for similar items in EconPapers)
Date: 2022-08
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Citations: View citations in EconPapers (18)

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https://www.princeton.edu/~mkolesar/papers/contamination.pdf

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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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2022-15

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