Robust Q-Learning. Issue 533 (2nd January 2021)

• Record Type: Journal Article
• Authors: Ertefaie, Ashkan; McKay, James R.; Oslin, David; Strawderman, Robert L.
• Is Part Of: Journal of the American Statistical Association. Volume 116:Issue 533(2021)
• Page Start: 368
• Page End: 381
• Publication Date: 2021-01-02
• Subjects: Cross-fitting -- Data-adaptive techniques -- Dynamic treatment strategies -- Residual confounding; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2020.1753522
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract : Abstract– 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.