AI RESEARCH
Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies
arXiv CS.LG
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ArXi:2603.21485v1 Announce Type: new Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic.