  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 deeply-debiasing 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.
