Counterfactual Learning with General Data-generating Policies

Autor:	Narita, Yusuke, Okumura, Kyohei, Shimizu, Akihiro, Yata, Kohei
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Economics - Econometrics Statistics - Applications Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2104.12909
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.01925 Zobrazit plný text záznamu View this record from Arxiv