Optimal weighting for estimating generalized average treatment effects

Kallus Nathan () and Santacatterina Michele ()
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Kallus Nathan: School of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York 10044, New York, USA
Santacatterina Michele: Department of Population Health, New York University Grossman School of Medicine, New York 10016, New York, USA

Journal of Causal Inference, 2022, vol. 10, issue 1, 123-140

Abstract: In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regression modeling, but these may be sensitive to model misspecification, practical violations of positivity, or both. The contribution of this article is twofold. First, we formulate the generalized average treatment effect (GATE) to unify these causal estimands as well as their IPW estimates. Second, we develop a method based on Kernel optimal matching (KOM) to optimally estimate GATE and to find the GATE most easily estimable by KOM, which we term the Kernel optimal weighted average treatment effect. KOM provides uniform control on the conditional mean squared error of a weighted estimator over a class of models while simultaneously controlling for precision. We study its theoretical properties and evaluate its comparative performance in a simulation study. We illustrate the use of KOM for GATE estimation in two case studies: comparing spine surgical interventions and studying the effect of peer support on people living with HIV.

Keywords: average treatment effect; causal inference; covariate balance; misspecification; optimization; positivity (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:10:y:2022:i:1:p:123-140:n:7

DOI: 10.1515/jci-2021-0018

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