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

合作研究：同伴效应因果推断的理论和方法框架

• 批准号: 1712714
• 负责人: Jun Liu
• 金额: $ 23.87万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712714&HistoricalAwards=false
• 关键词: Collaborative; Research; Theoretical; Methodological; Frameworks

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.

了解朋友或同龄人如何相互作用和影响，往往是生物医学研究和社会科学的极大兴趣。 然而，目前还不完全清楚如何使用正式的统计框架来量化和发展对同伴效应的推断。 该项目将侧重于开发一个统计因果推理框架，以应对这些挑战，其目标是为涉及同行效应推理的广泛问题开发理论和方法工具。 本研究将运用这些方法对不同学历背景的大学生进行同伴效应的调查。 经典的潜在结果因果推理框架假设实验单元之间不存在干扰。 在一些实证研究中，干扰是一种令人讨厌的东西，会使分析复杂化，应该通过仔细的实验设计来避免。 然而，在许多应用领域中，存在组或网络结构，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具有重要意义的科学问题。 现有的文献讨论了对单元的外部干预，其中诱导干扰的网络、集群或群体是先验已知的。 新的框架允许因果推理的推理工具的发展与干扰的Fisherian，Neymanian和贝叶斯的观点。根据Fisherian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。根据奈曼观点，将开发基于随机化的点和区间估计，作为寻找最佳治疗分配的基础。 根据贝叶斯观点，将开发分层模型，以适应现实生活数据的复杂结构，并将来自多个群体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。该项目由社会、行为和经济科学理事会的数学科学和方法、测量和统计（MMS）计划部门支持。

项目成果

Testing Model Utility for Single Index Models Under High Dimension

高维下单指标模型的模型实用性测试

• DOI: 10.1007/978-3-030-69009-0_4
• 发表时间: 2021
• 期刊: Festschrift in Honor of R. Dennis Cook
• 作者: Lin, Qian;Zhao, Zhigen;Liu, Jun S
• 通讯作者: Liu, Jun S

On Posterior Consistency of Bayesian Factor Models in High Dimensions

• DOI: 10.1214/21-ba1281
• 发表时间: 2020-06
• 期刊: Bayesian Analysis
• 影响因子: 4.4
• 作者: Yucong Ma;Jun S. Liu

Randomization Inference for Peer Effects

• DOI: 10.1080/01621459.2018.1512863
• 发表时间: 2019-04-09
• 期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• 影响因子: 3.7
• 作者: Li, Xinran;Din, Peng;Liu, Jun S.
• 通讯作者: Liu, Jun S.

Bayesian Analysis of Rank Data with Covariates and Heterogeneous Rankers

• DOI: 10.1214/20-sts818
• 发表时间: 2022-02-01
• 期刊: STATISTICAL SCIENCE
• 影响因子: 5.7
• 作者: Li, Xinran;Yi, Dingdong;Liu, Jun S.
• 通讯作者: Liu, Jun S.

Kernel-Based Partial Permutation Test for Detecting Heterogeneous Functional Relationship

用于检测异质函数关系的基于内核的部分排列测试

• DOI: 10.1080/01621459.2021.2000867
• 发表时间: 2023
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Li, Xinran;Jiang, Bo;Liu, Jun S.
• 通讯作者: Liu, Jun S.