Counterfactual Learning with General Data-Generating Policies

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.

Authors

• Yusuke Narita Yale University
• Kyohei Okumura Northwestern University
• Akihiro Shimizu Mercari, Inc.,
• Kohei Yata University of Wisconsin-Madison

Keywords

• APP: Business/Marketing/Advertising/E-Commerce
• ML: Causal Learning
• ML: Online Learning & Bandits
• ML: Reinforcement Learning Algorithms
• ML: Reinforcement Learning Theory