Contamination Bias in Linear Regressions / Paul Goldsmith-Pinkham, Peter Hull, Michal Kolesár.

Format:

Book

Author/Creator:

Goldsmith-Pinkham, Paul.

Contributor:

National Bureau of Economic Research.

Hull, Peter.

Kolesár, Michal.

Series:

Working Paper Series (National Bureau of Economic Research) no. w30108.

NBER working paper series no. w30108

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2022.

Summary:

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.

Notes:

Print version record

June 2022.