Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign
Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign
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DOI：
10.1093/pan/mpq035
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发表时间：
2011-12-01
影响因子：
5.4
通讯作者：
Strauss, Aaron
中科院分区：
文献类型：
--
作者：
Imai, Kosuke;Strauss, Aaron
尽管越来越多的政治学家正在进行随机实验，但他们中的许多人只报告了平均治疗效果，而没有系统地探索治疗效果在亚人群中的变化。从科学的角度来看，这是不幸的，因为异质性治疗效果可以提供额外的实质性见解。从政策制定者的角度来看，这种现状也是有问题的，因为这些研究并没有确定哪些治疗是有效的。在本文中，我们提出了一个正式的两步框架，首先从随机实验中识别异质治疗效果，然后使用该信息推导出关于应该向谁提供哪种治疗的最佳策略。我们提出的方法避免了在该学科常规进行的事后亚组分析中可能出现错误发现的风险。我们在投票随机场实验的背景下讨论了我们的方法，并展示了所提出的两步框架如何应用于现实世界的设置。
Although a growing number of political scientists are conducting randomized experiments, many of them only report the average treatment effects and do not systematically explore the variation in treatment effects across subpopulations. This is unfortunate from a scientific point of view because heterogeneous treatment effects can provide additional substantive insights. This current state of affairs is also problematic from a policy makers' perspective since such studies do not identify subgroups for which treatments are effective. In this paper, we propose a formal two-step framework that first identifies heterogeneous treatment effects from a randomized experiment and then uses this information to derive an optimal policy about which treatment should be given to whom. Our proposed method avoids the risk of false discoveries that are likely in post hoc subgroup analysis routinely conducted in the discipline. We discuss our methodology in the context of getout-the-vote randomized field experiments and show how the proposed two-step framework can be applied in real-world settings.