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

在生物医学研究和社会科学中，了解朋友或同伴如何相互作用和相互影响通常是非常有趣的。然而，目前尚不完全清楚如何使用正式的统计框架来量化和发展同伴效应的推论。该项目将侧重于开发一个统计因果推理框架来应对这些挑战，其目标是为涉及对等效应推理的广泛问题开发理论和方法工具。这些方法将应用于不同学术背景大学生的同伴效应研究。该研究可为决策者提供重要的指导。因果推理的经典潜在结果框架假设实验单元之间没有干扰。在一些实证研究中，干扰是一个麻烦，使分析复杂化，应该通过仔细的实验设计来避免。然而，在许多应用领域，群体或网络结构存在，并可能造成单位之间的干扰。在这些应用中，干扰不再是一个麻烦，因为研究干扰的因果效应模式是一个对政策或决策有重要影响的科学问题。现有的文献讨论了外部干预的单位，其中网络，集群或群体，诱导干扰是已知的先验。新的框架允许开发推理工具，用于从fisher， Neymanian和Bayesian角度进行干扰的因果推理。根据费舍尔的观点，随机化检验将用于检测与尖锐零假设的偏差，而不施加进一步的结构性假设。在内曼的观点下，基于随机化的点和区间估计器将被开发，作为寻找最优治疗分配的基础。在贝叶斯的观点下，层次模型将被开发以适应现实数据的复杂结构，并将来自多个具有纵向结果的群体的信息结合起来。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、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.