Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler, Jeremy Ferwerda, Jens Hainmueller, Michael Hotard
arXiv 25 Apr 2026 · Machine Learning
arXiv:2605.06686 · PDF · DOI · OpenAlex · Extracted main text
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).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bansak, K., Ferwerda, J., Hainmueller, J., Dillon, A., Hangartner, D… (2018) Improving refugee integration through data-driven algorithmic assignment self | 0.977 | 15 | 6 | 93% |
| 2 | Andrews, I., Kitagawa, T., and McCloskey, A (2024) Inference on winners | 0.644 | 2 | 2 | 100% |
| 3 | Bansak, K., Lee, S., Manshadi, V., Niazadeh, R., and Paulson, E (2026) Dynamic matching with post-allocation service and its application to refugee resettlement self | 0.644 | 2 | 2 | 100% |
| 4 | Bastani, H., Bastani, O., and McLaughlin, B (2026) Winner's curse drives false promises in data-driven decisions: A case study in refugee matching | 0.511 | 2 | 1 | 100% |
| 5 | Acharya, A., Bansak, K., and Hainmueller, J (2022) Combining outcome-based and preference-based matching: A constrained priority mechanism self | 0.405 | 1 | 1 | 100% |
| 6 | Ahani, N., Andersson, T., Martinello, A., Teytelboym, A., and Trapp,… (2021) Placement optimization in refugee resettlement | 0.405 | 1 | 1 | 100% |
| 7 | Ahani, N., Gölz, P., Procaccia, A. D., Teytelboym, A., and Trapp, A. C (2024) Dynamic placement in refugee resettlement | 0.405 | 1 | 1 | 100% |
| 8 | Bansak, K., Paulson, E., and Rothenhäusler, D (2024) Learning under random distributional shifts self | 0.405 | 1 | 1 | 100% |
| 9 | Bansak, K. and Paulson, E (2024) Outcome-driven dynamic refugee assignment with allocation balancing self | 0.405 | 1 | 1 | 100% |
| 10 | Efron, B (2011) Tweedie’s formula and selection bias | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 16 scored citations.