Conformal Off-policy Prediction

TL;DR

This paper introduces a new conformal prediction method for off-policy evaluation that provides reliable interval estimates for a policy's return, accounting for variability and enabling valid uncertainty quantification.

Contribution

It develops a novel conformal prediction approach for off-policy evaluation that produces valid prediction intervals for individual return estimates from any initial state.

Findings

Method produces valid, reliable prediction intervals.

Validated on synthetic and real short-video platform data.

Outperforms existing point estimators in uncertainty quantification.

Abstract

Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. In this paper, we develop a novel procedure to produce reliable interval estimators for a target policy's return starting from any initial state. Our proposal accounts for the variability of the return around its expectation, focuses on the individual effect and offers valid uncertainty quantification. Our main idea lies in designing a pseudo policy that generates subsamples as if they were sampled from the target policy so that existing conformal prediction algorithms are applicable to prediction interval construction. Our methods are justified by theories, synthetic data and real data from short-video platforms.