Augmented Inverse Probability Weighting and the Double Robustness Property

Abstract

This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a “doubly robust” method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy. Highlights • Average treatment effects are often estimated by regression or inverse probability weighting methods, but both are vulnerable to bias. • The augmented inverse probability weighted estimator is an easy-to-use method for average treatment effects that can be less biased because of the double robustness property.