EconPapers    
Economics at your fingertips  
 

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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.rieti.go.jp/jp/publications/dp/22e097.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:22097

Access Statistics for this paper

More papers in Discussion papers from Research Institute of Economy, Trade and Industry (RIETI) Contact information at EDIRC.
Bibliographic data for series maintained by TANIMOTO, Toko ().

 
Page updated 2023-01-02
Handle: RePEc:eti:dpaper:22097