Learning Optimal Distributionally Robust Individualized Treatment Rules

• 成果类型: Article
• 署名作者: Mo, Weibin; Qi, Zhengling; Liu, Yufeng
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; George Washington University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1796359
• 发表日期: 2021
• 页码: 659-674
• 关键词: hiv; DECISION; trials

摘要

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and testing distributions are the same. However, the estimated optimal ITR may have poor generalizability when the training and testing distributions are not identical. In this article, we consider the problem of finding an optimal ITR from a restricted ITR class where there are some unknown covariate changes between the training and testing distributions. We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are close to the training distribution. The resulting DR-ITR can guarantee the performance among all such distributions reasonably well. We further propose a calibrating procedure that tunes the DR-ITR adaptively to a small amount of calibration data from a target population. In this way, the calibrated DR-ITR can be shown to enjoy better generalizability than the standard ITR based on our numerical studies.for this article are available online.