# Off-Policy Evaluation and Learning for Matching Markets

arXiv: https://arxiv.org/abs/2507.13608 · HTML: https://mlsanity.com/paper/2507.13608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 25 of 25)

- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [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)
- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Doubly robust off-policy evaluation with shrinkage](https://mlsanity.com/paper/1907.09623/) (2019)
- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Adaptive Estimator Selection for Off-Policy Evaluation](https://mlsanity.com/paper/2002.07729/) (2020)
- [Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling](https://mlsanity.com/paper/2305.08062/) (2023)
- [Evaluating the Robustness of Off-Policy Evaluation](https://mlsanity.com/paper/2108.13703/) (2021)
- [Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction](https://mlsanity.com/paper/2402.02171/) (2024)
- [Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation](https://mlsanity.com/paper/2311.18207/) (2023)
- [Optimizing Rankings for Recommendation in Matching Markets](https://mlsanity.com/paper/2106.01941/) (2021)
- [Local Policy Improvement for Recommender Systems](https://mlsanity.com/paper/2212.11431/) (2022)
- [Long-term Off-Policy Evaluation and Learning](https://mlsanity.com/paper/2404.15691/) (2024)
- [Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits](https://mlsanity.com/paper/2408.11202/) (2024)
- [Reciprocal Recommendation System for Online Dating](https://mlsanity.com/paper/1501.06247/) (2015)
- [Online Dating Recommendations: Matching Markets and Learning Preferences](https://mlsanity.com/paper/1401.8042/) (2014)
- [Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://mlsanity.com/paper/2408.09748/) (2024)
- [Matching Theory-based Recommender Systems in Online Dating](https://mlsanity.com/paper/2208.11384/) (2022)
- [Reciprocal Sequential Recommendation](https://mlsanity.com/paper/2306.14712/) (2023)
- [Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets](https://mlsanity.com/paper/2306.09060/) (2023)
- [Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation](https://mlsanity.com/paper/2309.01120/) (2023)
- [RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating](https://mlsanity.com/paper/2011.12586/) (2020)
- [A Best-of-Both Approach to Improve Match Predictions and Reciprocal Recommendations for Job Search](https://mlsanity.com/paper/2409.10992/) (2024)

## Built on by (most cited first, 2 of 2)

- [Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching](https://mlsanity.com/paper/2602.15752/) (2026)
- [Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating](https://mlsanity.com/paper/2608.18058/) (2026)

## Abstract (arXiv)

Matching users based on mutual preferences is a fundamental aspect of services driven by reciprocal recommendations, such as job search and dating applications. Although A/B tests remain the gold standard for evaluating new policies in recommender systems for matching markets, it is costly and impractical for frequent policy updates. Off-Policy Evaluation (OPE) thus plays a crucial role by enabling the evaluation of recommendation policies using only offline logged data naturally collected on the platform. However, unlike conventional recommendation settings, the large scale and bidirectional nature of user interactions in matching platforms introduce variance issues and exacerbate reward sparsity, making standard OPE methods unreliable. To address these challenges and facilitate effective offline evaluation, we propose novel OPE estimators, textitDiPS and textitDPR, specifically designed for matching markets. Our methods combine elements of the Direct Method (DM), Inverse Propensity Score (IPS), and Doubly Robust (DR) estimators while incorporating intermediate labels, such as initial engagement signals, to achieve better bias-variance control in matching markets. Theoretically, we derive the bias and variance of the proposed estimators and demonstrate their advantages over conventional methods. Furthermore, we show that these estimators can be seamlessly extended to offline policy learning methods for improving recommendation policies for making more matches. We empirically evaluate our methods through experiments on both synthetic data and A/B testing logs from a real job-matching platform. The empirical results highlight the superiority of our approach over existing methods in off-policy evaluation and learning tasks for a variety of configurations.