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An Introduction to the Augmented Inverse Propensity Weighted Estimator

Published online by Cambridge University Press:  04 January 2017

Adam N. Glynn  and
Kevin M. Quinn
Adam N. Glynn*
Affiliation:
Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138
Kevin M. Quinn
Affiliation:
UC Berkeley School of Law, 490 Simon Hall, Berkeley, CA 94720-7200. e-mail: kquinn@law.berkeley.edu
*
e-mail: aglynn@iq.harvard.edu (corresponding author)
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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.

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 1 , Winter 2010 , pp. 36 - 56
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors's note: We thank the editors and three anonymous referees for helpful comments on an earlier draft of this paper. An R package that implements the estimators discussed in this paper is available at http://cran.r-project.org/as a contributed package with the name CausalGAM.

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