# Logging Policy Design for Off-Policy Evaluation

arXiv: https://arxiv.org/abs/2605.15108 · HTML: https://mlsanity.com/paper/2605.15108/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 4 of 4)

- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification](https://mlsanity.com/paper/2402.10592/) (2024)
- [Regret Minimization and Statistical Inference in Online Decision Making with High-dimensional Covariates](https://mlsanity.com/paper/2411.06329/) (2024)
- [Exploiting Similarities in A/B Testing with Off-Policy Estimation](https://mlsanity.com/paper/2506.10677/) (2025)

## Built on by (most cited first, 4 of 4)

- [When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide](https://mlsanity.com/paper/2608.12489/) (2026)
- [Beyond "AI Helps Humans": Decision-Targeted Evaluation Design for Human-Agent Teams in the Agentic Era](https://mlsanity.com/paper/2609.05527/) (2026)
- [Tail-Influence Sampling for CVaR Policy Evaluation](https://mlsanity.com/paper/2609.38096/) (2026)
- [What a Data Release Must Preserve for Off-Policy Evaluation](https://mlsanity.com/paper/2610.15283/) (2026)

## Abstract (arXiv)

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.