Off-Policy Evaluation with Out-of-Sample Guarantees
Abstract
We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or negative reward) and the main problem is making valid inferences about its out-of-sample loss when the past data was observed under a different and possibly unknown policy. Using a sample-splitting method, we show that it is possible to draw such inferences with finite-sample coverage guarantees about the entire loss distribution, rather than just its mean. Importantly, the method takes into account model misspecifications of the past policy - including unmeasured confounding. The evaluation method can be used to certify the performance of a policy using observational data under a specified range of credible model assumptions.
Keywords
Cite
@article{arxiv.2301.08649,
title = {Off-Policy Evaluation with Out-of-Sample Guarantees},
author = {Sofia Ek and Dave Zachariah and Fredrik D. Johansson and Petre Stoica},
journal= {arXiv preprint arXiv:2301.08649},
year = {2023}
}