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, Peking 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 Peking University.
翻译:先前许多因果推断研究不需要任何干扰,也就是说,一个单元的潜在结果并不取决于其他单元的处理情况。然而,当一个单元与同一组或组群中的其他单元发生互动时,这种不干预的假设变得不合理。在激励性申请中,北京大学通过两个渠道接纳学生:大学入学考试(又称高考)和建议(通常以各种学科的奥林匹克学为基础)。大学随机分配学生宿舍,每个宿舍容纳四个学生。同一宿舍内的学生住在一起,有着广泛的互动。因此,很可能存在同侪效应,而不干涉的假设不成立。重要的是要理解同侪效应,因为它们为未来的室友分配提供有用的指导,以提高学生的成绩。我们利用潜在结果界定同侪效应。我们然后提出一个随机化推论框架,以研究同侪和同侪类型任意数目的同侪效应。我们的推论程序并不假定结果分配的任何参数模型。我们的分析为平京大学的决策者提供了有用的实用指导。