An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan
提出一种自适应目标处理分配方法（Tempered Thompson算法），并在约旦的现场实验中测试了小额现金、信息和心理支持对叙利亚难民和本地求职者就业的影响，发现目标分配使就业率提高1个百分点（20%），且现金对叙利亚人的就业和收入有显著效果。
Abstract We introduce an adaptive targeted treatment assignment methodology for field experiments. Our Tempered Thompson Algorithm balances the goals of maximizing the precision of treatment effect estimates and maximizing the welfare of experimental participants. A hierarchical Bayesian model allows us to adaptively target treatments. We implement our methodology in Jordan, testing policies to help Syrian refugees and local jobseekers to find work. The immediate employment impacts of a small cash grant, information and psychological support are small, but targeting raises employment by 1 percentage-point (20%). After 4 months, cash has a sizable effect on employment and earnings of Syrians.