Off-Policy Evaluation with Out-of-Sample Guarantees
This enables certifying policy performance with observational data under credible assumptions, addressing a key challenge in off-policy evaluation for fields like healthcare or economics.
The paper tackles the problem of evaluating a decision policy's performance using observational data from a different policy, and shows that a sample-splitting method can provide finite-sample coverage guarantees for the entire loss distribution, even under model misspecifications like unmeasured confounding.
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.