Covariate Balancing Value Estimation for Optimal Individualized Treatment Rules

Abstract

Learning an optimal individualized treatment rule depends on reliable value comparisons across the candidate class. Standard doubly robust estimators are consistent when either the propensity score or outcome regression model is correctly specified, but they do not directly control the remaining bias in value estimation when both models are misspecified. In this paper, we propose a covariate balancing doubly robust estimator that combines propensity score estimation based on covariate balancing with an outcome regression component selected using an empirical variance criterion based on the influence function. Using prespecified covariate functions, the balancing procedure induces an effective balancing space to which the weighted propensity score error is orthogonal. Consequently, the proposed value estimator is consistent if either the propensity score model is correct or the rule-relevant outcome regression error lies in this space. The latter condition does not require a correctly specified outcome regression model and can hold even when both working models are misspecified. Under correct propensity score specification, the estimator has the smallest asymptotic variance within the proposed covariate balancing doubly robust class. Simulations evaluate value estimation in finite samples and the regret of learned rules, and an application to a leukemia dataset illustrates the proposed method in practice.