统计与数据科学系系列学术报告之四百八十四期

时间： 2025年9月26日（周五）10:00-10:45

主持人：复旦大学管理学院统计与数据科学系沈娟副教授

地点：史带楼303室

报告人：刘汉中副教授

清华大学

题目：Estimation and inference of average treatment effects under heterogeneous additive treatment effect model

摘要：Randomized experiments are the gold standard for estimating treatment effects, yet network interference challenges the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. While cluster randomized experiments mitigate this bias, they encounter limitations in handling network complexity and fail to distinguish between direct and indirect effects. To address these challenges, we develop a design-based asymptotic theory for the existing Horvitz--Thompson estimators of the direct, indirect, and global average treatment effects under Bernoulli trials. We assume the heterogeneous additive treatment effect model with a hidden network that drives interference. Observing that these estimators are inconsistent in dense networks, we introduce novel eigenvector-based regression adjustment estimators to ensure consistency. We establish the asymptotic normality of the proposed estimators and provide conservative variance estimators under the design-based inference framework, offering robust conclusions independent of the underlying stochastic processes of the network and model parameters. Our method's adaptability is demonstrated across various interference structures, including partial interference and local interference in a two-sided marketplace. Numerical studies further illustrate the efficacy of the proposed estimators, offering practical insights into handling network interference.

个人简介：刘汉中，清华大学统计与数据科学系长聘副教授，北京大学统计学博士，美国加州大学伯克利分校博士后。主要研究兴趣包括高维统计推断、机器学习和因果推断。研究成果发表于PNAS、JASA、Biometrika等顶级期刊。主持国家自然科学基金青年基金项目1项，面上项目1项，参与国家重点研发计划2项。担任全国工业统计学教学研究会理事、北京应用统计学会理事等。