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Title: Robust Q-learning Authors:  Robert Strawderman - University of Rochester (United States) [presenting]
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 significant efficiency loss. We propose a robust Q-learning approach that allows estimating such nuisance parameters using data-adaptive techniques. Methodology, asymptotics and simulations will be summarized and highlight the utility of the proposed methods in practice. Data from the ``Extending Treatment Effectiveness of Naltrexone'' multistage randomized trial will be used to illustrate the proposed methods.