Distributional Robust Batch Contextual Bandits

Policy learning using historical observational data is an important problem that has found widespread applications. Examples include selecting offers, prices, advertisements to send to customers, as well as selecting which medication to prescribe to a patient. However, existing literature rests on the crucial assumption that the future environment where the learned policy will be deployed is the same as the past environment that has generated the data--an assumption that is often false or too coarse an approximation. In this paper, we lift this assumption and aim to learn a distributional robust policy with incomplete (bandit) observational data. We propose a novel learning algorithm that is able to learn a robust policy to adversarial perturbations and unknown covariate shifts. We first present a policy evaluation procedure in the ambiguous environment and then give a performance guarantee based on the theory of uniform convergence. Additionally, we also give a heuristic algorithm to solve the distributional robust policy learning problems efficiently. Finally, we demonstrate the robustness of our methods in the synthetic and real-world datasets.