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Entropy Balancing is Doubly Robust

Author

Listed:
  • Zhao Qingyuan

    (Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA)

  • Percival Daniel

    (Google Inc., Mountain View, CA, USA)

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.

Suggested Citation

  • Zhao Qingyuan & Percival Daniel, 2017. "Entropy Balancing is Doubly Robust," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-19, March.
    • Handle: RePEc:bpj:causin:v:5:y:2017:i:1:p:19:n:4
    DOI: 10.1515/jci-2016-0010
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    Cited by:

    1. Anna Adamecz-Völgyi & Morag Henderson & Nikki Shure, 2023. "The labor market returns to “first-in-family” university graduates," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(3), pages 1395-1429, July.
    2. Ibtihal Ferwana & Lav R. Varshney, 2022. "Optimal Recovery for Causal Inference," Papers 2208.06729, arXiv.org, revised Dec 2023.
    3. Ben Jann, 2020. "Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment," University of Bern Social Sciences Working Papers 35, University of Bern, Department of Social Sciences, revised 31 Aug 2020.
    4. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    5. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
    6. Muffert, Johanna & Riphahn, Regina T., 2025. "Long-Run Career Outcomes of Multiple Job Holding," IZA Discussion Papers 17605, Institute of Labor Economics (IZA).

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