# Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices

arXiv: https://arxiv.org/abs/2605.06686 · HTML: https://mlsanity.com/paper/2605.06686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Dynamic Placement in Refugee Resettlement](https://mlsanity.com/paper/2105.14388/) (2021)
- [Group fairness in dynamic refugee assignment](https://mlsanity.com/paper/2301.10642/) (2023)
- [A Flexible Defense Against the Winner's Curse](https://mlsanity.com/paper/2411.18569/) (2024)
- [Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing](https://mlsanity.com/paper/2007.03069/) (2020)
- [Learning under random distributional shifts](https://mlsanity.com/paper/2306.02948/) (2023)
- [Winner's Curse Drives False Promises in Data-Driven Decisions: A Case Study in Refugee Matching](https://mlsanity.com/paper/2602.08892/) (2026)
- [Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement](https://mlsanity.com/paper/2410.22992/) (2024)
- [CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets](https://mlsanity.com/paper/2508.11144/) (2025)
- [A Dual Perspective on Decision-Focused Learning: Scalable Training via Dual-Guided Surrogates](https://mlsanity.com/paper/2511.04909/) (2025)
- [Migration as Submodular Optimization](https://mlsanity.com/paper/1809.02673/) (2018)

## Abstract (arXiv)

Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfactual impact and test the robustness of our results, we employ several evaluation methods, including inverse probability weighting (IPW) and multiple variants of augmented inverse probability weighting (AIPW). We also consider various modifications, including alternative modeling architectures and different assignment procedures. The impact estimates remain consistent in magnitude in all scenarios as well as statistically significant in most cases. Furthermore, the estimates are also consistent with the results originally presented in Bansak et al. (2018).