Abstract:
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.