标题： 数据融合以预测长期项目影响 显示英文标题

标题： Data fusion for predicting long-term program impacts

摘要： 政策制定者经常需要了解项目长期影响的信息，而这些信息在做出决策时可能无法获得。 我们展示如何使用数据融合方法来解决缺失最终结果的问题，并在所需数据可用之前预测干预措施的长期影响。 我们通过将干预措施的数据与辅助的长期数据进行拼接，然后利用短期替代结果来填补缺失的长期结果，并通过复制方法来近似不确定性。 我们使用模拟来检验该方法的性能，并在案例研究中应用该方法。 具体而言，我们将俄勒冈州健康保险实验的数据与国家纵向死亡率研究的数据进行融合，并估计有资格申请补贴健康保险将导致长期死亡率的统计学上显著的改善。 显示英文摘要

摘要： Policymakers often require information on programs' long-term impacts that is not available when decisions are made. We demonstrate how data fusion methods may be used address the problem of missing final outcomes and predict long-run impacts of interventions before the requisite data are available. We implement this method by concatenating data on an intervention with auxiliary long-term data and then imputing missing long-term outcomes using short-term surrogate outcomes while approximating uncertainty with replication methods. We use simulations to examine the performance of the methodology and apply the method in a case study. Specifically, we fuse data on the Oregon Health Insurance Experiment with data from the National Longitudinal Mortality Study and estimate that being eligible to apply for subsidized health insurance will lead to a statistically significant improvement in long-term mortality.