Contamination Bias in Linear Regressions
研究了多元处理线性回归中，估计值会被其他处理的非凸平均效应污染的问题，并提出了三种避免污染偏差的方法。对实证研究者判断回归结果可靠性有参考价值。
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.