Collaborative Research: Theoretical and Methodological Frameworks for Causal Inference of Peer Effects

了解朋友或同龄人是如何相互作用和影响的，往往是生物医学研究和社会科学的极大兴趣。然而，如何使用正式的统计框架来量化和发展对同行效应的推断并不完全清楚。该项目将侧重于开发一个统计因果推断框架，以应对这些挑战，目标是为涉及同行效应推断的广泛问题开发理论和方法工具。这些方法将被用于调查不同学历的大学生之间的同伴效应。该研究可以为决策和政策制定者提供重要的指导。经典的因果推理潜在结果框架假设实验单位之间不存在干扰。在一些实证研究中，干扰是一种使分析复杂化的麻烦，应该通过仔细的实验设计来避免。然而，在许多应用领域中，群或网络结构存在，并且可能导致单元之间的干扰。在这些应用中，干扰不再是一种麻烦，因为研究干扰的因果关系模式是一个对政策或决策具有重要影响的科学问题。现有文献讨论了对单位的外部干预，其中引起干扰的网络、集群或群体是先验已知的。新的框架允许开发用于因果推理的推理工具，并从费舍尔、内曼和贝叶斯的角度进行干预。根据菲舍尔的观点，随机化测试将被用来检测与尖锐零假设的偏差，而不会强加进一步的结构性假设。根据Neymanian的观点，将开发基于随机化的点和区间估计器，作为寻找最佳治疗分配的基础。在贝叶斯观点下，将开发分层模型，以适应现实生活数据的复杂结构，并将来自多个组的信息与纵向结果结合起来。该项目还将导致开源R软件的开发。该项目由数学科学部和社会、行为和经济科学局的方法论、测量和统计(MMS)项目支持。

Understanding how friends or peers interact and affect each other is often of great interest in biomedical studies and the social sciences. However, it is not entirely clear how to quantify and develop inference for peer effects using a formal statistical framework. This project will focus on the development of a statistical causal inference framework to address these challenges, with the goal of developing both theoretical and methodological tools for a wide class of questions involving inference of peer effects. The methods will be applied to investigate peer effects among university students with different academic backgrounds. The research could provide important guidance for decision and policy makers.The classical potential outcomes framework for causal inference assumes no interference among experimental units. In some empirical studies, interference is a nuisance that complicates analysis and should be avoided by careful experimental design. In many applied fields, however, group or network structures exist and could cause interference among units. Interference is no longer a nuisance in these applications, because studying the pattern of causal effects with interference is the scientific question of interest with important implications for policy or decision making. The existing literature discusses external interventions on the units, where the networks, clusters or groups that induce interference are known a priori. The new framework allows for the development of inferential tools for causal inference with interference from the Fisherian, Neymanian, and Bayesian perspectives. Under the Fisherian view, randomization tests will be used to detect deviations from the sharp null hypothesis without imposing further structural assumptions. Under the Neymanian view, randomization-based point and interval estimators, which serve as the basis for finding optimal treatment assignments will be developed. Under the Bayesian view, hierarchical models will be developed to accommodate complex structures of real-life data and incorporate information from multiple groups with longitudinal outcomes. The project will also lead to the development of open-source R software. This project is supported by the Division of Mathematical Sciences and the Methodology, Measurement, and Statistics (MMS) Program in the Directorate for Social, Behavioral, and Economic Sciences.