Robust and Efficient Estimation of Average Treatment Effects in Cross-Sectional Studies: An Introduction to the Augmented Inverse Propensity Weighted Estimator

In this paper we discuss an estimator for average treatment effects known as the augmented inverse propensity weighted (AIPW). This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator is dramatically superior to the other estimators in many situations and at least as good as the other estimators across a wide range of data generating processes. ∗Department of Government and The Institute for Quantitative Social Sciences Harvard University, 1737 Cambridge Street, Cambridge, MA 02138. [email protected] †Department of Government and The Institute for Quantitative Social Sciences Harvard University, 1737 Cambridge Street, Cambridge, MA 02138. kevin [email protected]