Entropy Balancing is Doubly Robust

Zhao Qingyuan () and Percival Daniel ()
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Zhao Qingyuan: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
Percival Daniel: Google Inc., Mountain View, CA, USA

Journal of Causal Inference, 2017, vol. 5, issue 1, 19

Abstract: Covariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.

Keywords: causal inference; double robustness; exponential tilting; convex optimization; survey sampling (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:5:y:2017:i:1:p:19:n:4

DOI: 10.1515/jci-2016-0010

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