  We develop a generic data-driven method for estimator selection in off-policy
policy evaluation settings. We establish a strong performance guarantee for the
method, showing that it is competitive with the oracle estimator, up to a
constant factor. Via in-depth case studies in contextual bandits and
reinforcement learning, we demonstrate the generality and applicability of the
method. We also perform comprehensive experiments, demonstrating the empirical
efficacy of our approach and comparing with related approaches. In both case
studies, our method compares favorably with existing methods.
