An Introduction to the Augmented Inverse Propensity Weighted Estimator

In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a bina...

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Bibliographic Details
Published in:Political analysis 2010-01, Vol.18 (1), p.36-56
Main Authors: Glynn, Adam N., Quinn, Kevin M.
Format: Article
Language:English
Subjects:
Consistent estimators
Control units
Econometrics
Estimation
Estimation bias
Estimators
Linear regression
Monte Carlo simulation
Outcome variables
Political analysis
Regression Analysis
Sample size
Sampling bias
Unbiased estimators
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Summary:In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. 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. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample 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 has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.
ISSN:1047-1987
1476-4989
DOI:10.1093/pan/mpp036