Robust Q-learning
This work addresses a specific issue in causal inference for personalized medicine, offering an incremental improvement over standard Q-learning methods.
The authors tackled the problem of model misspecification in Q-learning for dynamic treatment strategies, which can cause residual confounding and efficiency loss, by proposing a robust Q-learning method that uses data-adaptive techniques for nuisance parameter estimation, resulting in improved performance as demonstrated in simulation studies and a real-world trial application.
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" multi-stage randomized trial to illustrate our proposed methods.