Off-policy Evaluation with General Logging Policies: Implementation at Mercari

Yusuke Narita, Kyohei Okumura, Akihiro Shimizu and Kohei Yata

Discussion papers from Research Institute of Economy, Trade and Industry (RIETI)

Abstract: Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE method for a class of both full support and deficient support logging policies in contextual-bandit settings. This class includes deterministic bandit (such as Upper Confidence Bound) as well as deterministic decision-making based on supervised and unsupervised learning. We prove that our method's prediction converges in probability to the true performance of a counterfactual policy as the sample size increases. We validate our method with experiments on partly and entirely deterministic logging policies. Finally, we apply it to evaluate coupon targeting policies by a major online platform and show how to improve the existing policy.

Pages: 26 pages
Date: 2022-10
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Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:22097

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