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arXiv · 2405.15332

Cross-Validated Off-Policy Evaluation

Matej Cief·Branislav Kveton·Michal Kompan
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Abstract

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.

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Matej Cief, Branislav Kveton, Michal Kompan. 2024-05-24. Cross-Validated Off-Policy Evaluation. https://arxiv.org/abs/2405.15332

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