An Introduction to the Augmented Inverse Propensity Weighted Estimator

Posted: 25 Jan 2010

See all articles by Adam Glynn

Adam Glynn

Harvard University

Kevin M. Quinn

University of Michigan at Ann Arbor - Department of Political Science

Date Written: Winter 2010

Abstract

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.

Suggested Citation

Glynn, Adam and Quinn, Kevin M., An Introduction to the Augmented Inverse Propensity Weighted Estimator (Winter 2010). Political Analysis, Vol. 18, Issue 1, pp. 36-56, 2010. Available at SSRN: https://ssrn.com/abstract=1541024 or http://dx.doi.org/10.1093/pan/mpp036

Adam Glynn (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Kevin M. Quinn

University of Michigan at Ann Arbor - Department of Political Science ( email )

Ann Arbor, MI 48109
United States

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