Counterfactual Learning with General Data-generating Policies

• URL: http://arxiv.org/abs/2212.01925v1
• Date: Sun, 4 Dec 2022 21:07:46 GMT
• Title: Counterfactual Learning with General Data-generating Policies
• Authors: Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, Kohei Yata
• Abstract summary: We develop an OPE method for a class of full support and deficient support logging policies in contextual-bandit settings. We prove that our method's prediction converges in probability to the true performance of a counterfactual policy as the sample size increases.
• Score: 3.441021278275805
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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