Deeply-Debiased Off-Policy Interval Estimation

• URL: http://arxiv.org/abs/2105.04646v1
• Date: Mon, 10 May 2021 20:00:08 GMT
• Title: Deeply-Debiased Off-Policy Interval Estimation
• Authors: Chengchun Shi and Runzhe Wan and Victor Chernozhukov and Rui Song
• Abstract summary: Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy. Many applications would benefit significantly from having a confidence interval (CI) that quantifies the uncertainty of the point estimate. We propose a novel procedure to construct an efficient, robust, and flexible CI on a target policy's value.
• Score: 11.683223078990325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy. In addition to a point estimate, many applications would benefit significantly from having a confidence interval (CI) that quantifies the uncertainty of the point estimate. In this paper, we propose a novel procedure to construct an efficient, robust, and flexible CI on a target policy's value. Our method is justified by theoretical results and numerical experiments. A Python implementation of the proposed procedure is available at https://github.com/RunzheStat/D2OPE.

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