Randomization Inference for Peer Effects
Randomization Inference for Peer Effects
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DOI：
10.1080/01621459.2018.1512863
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发表时间：
2019-04-09
影响因子：
3.7
通讯作者：
Liu, Jun S.
中科院分区：
文献类型：
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作者：
Li, Xinran;Din, Peng;Liu, Jun S.
许多先前的因果推理研究不需要干扰，也就是说，一个单元的潜在结果不依赖于其他单元的处理。然而，当一个单元与同一组或集群中的其他单元交互时，这种无干扰假设变得不合理。在一份激励人心的申请中，一所中国顶尖大学通过两个渠道录取学生：高考（也被称为高考）和推荐（通常基于各个学科的奥数）。大学随机分配学生到宿舍，每个宿舍容纳四名学生。同一宿舍的学生住在一起，有广泛的互动。因此，很可能存在同伴效应，无干扰假设不成立。了解同伴效应是很重要的，因为它们为未来的室友分配提供了有用的指导，以提高学生的表现。我们用潜在的结果来定义同伴效应。然后，我们提出了一个基于随机化的推理框架，研究同行的影响与任意数量的同行和同行类型。我们的推理过程不假设任何参数模型的结果分布。我们的分析为大学的决策者提供了有益的实践指导。可以在网上找到。
Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts with other units in the same group or cluster. In a motivating application, a top Chinese university admits students through two channels: the college entrance exam (also known as Gaokao) and recommendation (often based on Olympiads in various subjects). The university randomly assigns students to dorms, each of which hosts four students. Students within the same dorm live together and have extensive interactions. Therefore, it is likely that peer effects exist and the no-interference assumption does not hold. It is important to understand peer effects, because they give useful guidance for future roommate assignment to improve the performance of students. We define peer effects using potential outcomes. We then propose a randomization-based inference framework to study peer effects with arbitrary numbers of peers and peer types. Our inferential procedure does not assume any parametric model on the outcome distribution. Our analysis gives useful practical guidance for policy makers of the university. for this article are available online.