Apr 25, 2026 · cs.LGJ/K move · Enter open · S save
Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler, Jeremy Ferwerda+2
Immigration Policy Lab, Stanford University · Department of Political Science, University of California, Berkeley · Technology and Operations Management Unit, Harvard Business School · Department of Statistics, Stanford University · Department of Government, Dartmouth College
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).