[2605.06686] Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices
[Submitted on 25 Apr 2026]
Title:Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices
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Abstract: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).
Comments: 13 pages, 2 figures, 10 tables
Subjects:
Machine Learning (cs.LG); Econometrics (econ.EM); Applications (stat.AP); Machine Learning (stat.ML)
Cite as: arXiv:2605.06686 [cs.LG]
(or arXiv:2605.06686v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.06686
arXiv-issued DOI via DataCite
Submission history
From: Kirk Bansak [view email] [v1] Sat, 25 Apr 2026 22:48:03 UTC (46 KB)
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