Review Articles

Multiply robust estimation for average treatment effect among treated

Lu Wang ,

Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

Peisong Han

Biostatistics Innovation Group, Gilead Sciences, Foster City, CA, USA

Pages | Received 06 Mar. 2023, Accepted 10 Nov. 2023, Published online: 15 Dec. 2023,
  • Abstract
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We propose a multiply robust estimator for the Average Treatment Effect Among the Treated (ATT). The proposed estimation procedure can simultaneously accommodate multiple working models for both the propensity score and the conditional mean of the counterfactual outcome given covariates. In addition, it can explicitly balance a set of user-specified moments of the covariate distributions between the treatment groups. The resulting estimator is consistent if any working model is correctly specified. With the data generating process typically unknown for observational studies, the proposed method provides substantial robustness against possible model misspecifications compared to existing estimators of the ATT. Simulation results show the excellent finite sample performance of the proposed estimator.


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To cite this article: Lu Wang & Peisong Han (2024) Multiply robust estimation for average treatment effect among treated, Statistical Theory and Related Fields, 8:1, 29-39, DOI: 10.1080/24754269.2023.2293554

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