Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects∗

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficiency bound even for samples of size 100. Pair matching exhibits similarly good performance in terms of bias, but has notably higher variance. Local linear and ridge matching are competitive with reweighting in terms of bias and variance, but only once n = 500. Nearest-neighbor, kernel, and blocking matching are not competitive. When overlap is close to failing, none of the estimators examined perform well and √ n -asymptotics may be a poor guide to finite sample performance. Trimming rules, commonly used in the face of problems with overlap, are effective only in settings with homogeneous treatment effects. JEL Classification: C14, C21, C52.