Logging Policy Design for Off-Policy Evaluation

Problem
This paper addresses a significant gap in the literature on off-policy evaluation (OPE), specifically focusing on the design of logging policies that minimize OPE error for target treatment policies. The authors highlight that while OPE allows for the evaluation of policies without live deployment, the accuracy of these evaluations is heavily influenced by the logging policy used to collect data. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a unifying framework for logging policy design that characterizes a fundamental tradeoff between reward concentration and coverage. They derive optimal logging policies under three scenarios: (i) when the target policy and reward distribution are known, (ii) when they are unknown, and (iii) when they are partially known through priors or noisy estimates at the time of logging. The framework emphasizes the importance of treatment selection in data gathering for OPE, providing theoretical foundations for optimal logging policies. The authors also present practical design principles for firms that may face operational constraints, allowing them to select logging policies that are not theoretically optimal but still effective.

Results
The paper demonstrates that optimal logging policies can significantly reduce OPE error compared to standard logging approaches. While specific numerical results are not disclosed in the abstract, the authors indicate that their theoretical framework provides actionable guidance for firms evaluating multiple candidate recommendation systems. The results underscore the critical role of logging policy design in improving the accuracy of OPE estimates, particularly in high-stakes environments.

Limitations
The authors acknowledge that their framework may not account for all real-world complexities, such as dynamic environments where the target policy may evolve over time. Additionally, the reliance on known or partially known distributions may limit applicability in scenarios where such information is not available. The practical design principles, while useful, may not fully capture the nuances of every operational constraint faced by firms.

Why it matters
This work has significant implications for the field of reinforcement learning and recommendation systems, particularly in contexts where live experimentation is costly or risky. By providing a structured approach to logging policy design, the authors enable practitioners to make informed decisions that enhance the reliability of OPE estimates. This can lead to more effective deployment of recommendation systems and improved user experiences. Furthermore, the insights gained from this research can inform future studies on OPE and logging policy design, potentially leading to advancements in the theoretical understanding of these concepts.

Authors: Connor Douglas, Joel Persson, Foster Provost
Source: arXiv:2605.15108
URL: https://arxiv.org/abs/2605.15108v1