Abstract: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. We characterize a fundamental reward-coverage tradeoff: concentrating probability mass on high-reward actions reduces variance but risks missing signal on actions the target policy may take. We propose a unifying framework for logging policy design and derive optimal policies in canonical informational regimes where the target policy and reward distribution are (i) known, (ii) unknown, and (iii) partially known through priors or noisy estimates at logging time. Our results provide actionable guidance for firms choosing among multiple candidate recommendation systems. We demonstrate the importance of treatment selection when gathering data for OPE, and describe theoretically optimal approaches when this is a firm's primary objective. We also distill practical design principles for selecting logging policies when operational constraints prevent implementing the theoretical optimum.
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Methodology (stat.ME) |
| MSC classes: | 62K05 |
| ACM classes: | G.3; I.2.6; I.2.8; H.3.3; H.2.8 |
| Cite as: | arXiv:2605.15108 [stat.ML] |
| (or arXiv:2605.15108v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15108 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Connor Douglas [view email]
[v1]
Thu, 14 May 2026 17:25:19 UTC (505 KB)