Robust Q-Learning

• 成果类型: Article
• 署名作者: Ertefaie, Ashkan; McKay, James R.; Oslin, David; Strawderman, Robert L.
• 署名单位: University of Rochester; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1753522
• 发表日期: 2021
• 页码: 368-381
• 关键词: dynamic treatment regimes; DESIGN; inference; strategies; selection

摘要

Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss. We propose a robust Q-learning approach which allows estimating such nuisance parameters using data-adaptive techniques. We study the asymptotic behavior of our estimators and provide simulation studies that highlight the need for and usefulness of the proposed method in practice. We use the data from the Extending Treatment Effectiveness of Naltrexone multistage randomized trial to illustrate our proposed methods. Supplementary materials for this article are available online.