AI RESEARCH
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
arXiv CS.LG
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ArXi:2412.08052v2 Announce Type: replace Off-policy evaluation (OPE) is critical for applying contextual bandit algorithms to high-stakes decision-making settings such as healthcare, where new treatment policies must be evaluated prior to deployment. Unfortunately, OPE techniques are inherently limited by the breadth of the available data, which may not be sufficient to evaluate the performance of a new policy. Recent work attempts to improve dataset coverage by adding expert-annotated counterfactual samples.