Cross-Validated Off-Policy Evaluation

We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.

Authors

• Matej Cief Brno University of Technology Kempelen Institute of Intelligent Technologies
• Branislav Kveton Adobe Research
• Michal Kompan Kempelen Institute of Intelligent Technologies

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