A novel evaluation methodology for assessing off-policy learning methods in contextual bandits

Abstract

We propose a novel evaluation methodology for assessing off-policy learning methods in contextual bandits. In particular, we provide a way to use data from any given Randomized Control Trial (RCT) to generate a range of observational studies with synthesized “outcome functions” that can match the user’s specified degrees of sample selection bias, which can then be used to comprehensively assess a given learning method. This is especially important in evaluating methods developed for precision medicine, where deploying a bad policy can have devastating effects. As the outcome function specifies the real-valued quality of any treatment for any instance, we can accurately compute the quality of any proposed treatment policy. This paper uses this evaluation methodology to establish a common ground for comparing the robustness and performance of the available off-policy learning methods in the literature.