# Meta Off-Policy Estimation

arXiv: https://arxiv.org/abs/2508.07914 · HTML: https://mlsanity.com/paper/2508.07914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Doubly Robust Policy Evaluation and Optimization](https://mlsanity.com/paper/1503.02834/) (2015)
- [More Robust Doubly Robust Off-policy Evaluation](https://mlsanity.com/paper/1802.03493/) (2018)
- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [Doubly robust off-policy evaluation with shrinkage](https://mlsanity.com/paper/1907.09623/) (2019)
- [Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation](https://mlsanity.com/paper/2008.07146/) (2020)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [Policy-Adaptive Estimator Selection for Off-Policy Evaluation](https://mlsanity.com/paper/2211.13904/) (2022)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [Optimal Baseline Corrections for Off-Policy Contextual Bandits](https://mlsanity.com/paper/2405.05736/) (2024)
- [Ad-load Balancing via Off-policy Learning in a Content Marketplace](https://mlsanity.com/paper/2309.11518/) (2023)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Delta-rm OPE: Off-Policy Estimation with Pairs of Policies](https://mlsanity.com/paper/2405.10024/) (2024)
- [Large-scale Validation of Counterfactual Learning Methods: A Test-Bed](https://mlsanity.com/paper/1612.00367/) (2016)
- [A Probabilistic Position Bias Model for Short-Video Recommendation Feeds](https://mlsanity.com/paper/2307.14059/) (2023)
- [OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators](https://mlsanity.com/paper/2405.17708/) (2024)
- [Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation](https://mlsanity.com/paper/2309.01120/) (2023)

## Abstract (arXiv)

Off-policy estimation (OPE) methods enable unbiased offline evaluation of recommender systems, directly estimating the online reward some target policy would have obtained, from offline data and with statistical guarantees. The theoretical elegance of the framework combined with practical successes have led to a surge of interest, with many competing estimators now available to practitioners and researchers. Among these, Doubly Robust methods provide a prominent strategy to combine value- and policy-based estimators. In this work, we take an alternative perspective to combine a set of OPE estimators and their associated confidence intervals into a single, more accurate estimate. Our approach leverages a correlated fixed-effects meta-analysis framework, explicitly accounting for dependencies among estimators that arise due to shared data. This yields a best linear unbiased estimate (BLUE) of the target policy's value, along with an appropriately conservative confidence interval that reflects inter-estimator correlation. We validate our method on both simulated and real-world data, demonstrating improved statistical efficiency over existing individual estimators.