  We propose a robust regression approach to off-policy evaluation (OPE) for
contextual bandits. We frame OPE as a covariate-shift problem and leverage
modern robust regression tools. Ours is a general approach that can be used to
augment any existing OPE method that utilizes the direct method. When
augmenting doubly robust methods, we call the resulting method Triply Robust.
We prove upper bounds on the resulting bias and variance, as well as derive
novel minimax bounds based on robust minimax analysis for covariate shift. Our
robust regression method is compatible with deep learning, and is thus
applicable to complex OPE settings that require powerful function
approximators. Finally, we demonstrate superior empirical performance across
the standard OPE benchmarks, especially in the case where the logging policy is
unknown and must be estimated from data.
