Off-policy confidence interval estimation with confounded Markov decision process

Shi, Chengchun, Zhu, Jin, Shen, Ye, Luo, Shikai, Zhu, Hongtu and Song, Rui (2022) Off-policy confidence interval estimation with confounded Markov decision process. Journal of the American Statistical Association. ISSN 0162-1459

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Abstract

This article is concerned with constructing a confidence interval for a target policy’s value offline based on a pre-collected observational data in infinite horizon settings. Most of the existing works assume no unmeasured variables exist that confound the observed actions. This assumption, however, is likely to be violated in real applications such as healthcare and technological industries. In this article, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy’s value is identifiable in a confounded Markov decision process. Based on this result, we develop an efficient off-policy value estimator that is robust to potential model misspecification and provide rigorous uncertainty quantification. Our method is justified by theoretical results, simulated and real datasets obtained from ridesharing companies. A Python implementation of the proposed procedure is available at https://github.com/Mamba413/cope.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/uasa20
Additional Information:	© 2022 The Author(s).
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	03 Aug 2022 08:42
Last Modified:	18 Apr 2024 01:15
URI:	http://eprints.lse.ac.uk/id/eprint/115774

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