验证了难民匹配政策评估结果在不同方法下的稳定性。
Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices
- 采用多种离策略评估方法检验难民匹配效果。
- 不同方法下影响估计值大小一致且多数显著。
- 结果与早期研究一致，适合政策评估参考。
先前研究探讨了难民匹配对提升难民成果的潜力，首次由Bansak等人（2018）提出。本文通过多种离策略评估方法，验证了美国难民匹配情境下反事实影响评估结果的稳健性。为估算反事实影响并测试结果稳健性，我们采用了逆概率加权（IPW）及多种增广逆概率加权（AIPW）变体，并考察了不同建模架构和分配流程的修改。所有情景下影响估计值在量级上保持一致，且多数情况下具有统计显著性。此外，估计结果与Bansak等（2018）最初报告的结果一致。
原文摘要 · Abstract (English)
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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