Offline Policy Evaluation with Out-of-Sample Guarantees
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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 or disutility (or negative reward) and the problem is to draw valid inferences about the out-of-sample loss of the specified policy when the past data is observed under a, possibly unknown, policy. Using a sample-splitting method, we show that it is possible to draw such inferences with finite-sample coverage guarantees that evaluate the entire loss distribution. 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 an explicitly specified range of credible model assumptions.
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