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Deeply-Debiased Off-Policy Interval Estimation

Chengchun Shi · Runzhe Wan · Victor Chernozhukov · Rui Song

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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 RunzheStat/D2OPE.

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