AI RESEARCH
Logging Policy Design for Off-Policy Evaluation
arXiv CS.LG
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ArXi:2605.15108v1 Announce Type: cross Off-policy evaluation (OPE) estimates the value of a target treatment policy (e.g., a recommender system) using data collected by a different logging policy. It enables high-stakes experimentation without live deployment, yet in practice accuracy depends heavily on the logging policy used to collect data for computing the estimate. We study how to design logging policies that minimize OPE error for given target policies.