时 间：2026年1月27 日（周二）10：00 – 11：30
地 点：中北理科大楼A1514室
报告人：朱雪宁 复旦大学副教授
主持人：郁淼淼 华东师范大学助理教授
摘 要：
We study estimation of the average treatment effect (ATE) from a single network in observational settings with interference. The weak cross-unit dependence is modeled via an endogenous peer-effect (network autoregressive) term that induces distance-decaying network dependence, relaxing the common finite-order interference to infinite interference. We propose a targeted minimum loss estimation (TMLE) procedure that removes plug-in bias from an initial estimator. The targeting step yields an adjustment direction that incorporates the network autoregressive structure and assigns heterogeneous, network-dependent weights to units. We find that the asymptotic leading term related to the covariates Xi can be formulated into a V-statistic whose order diverges with the network degrees. A novel limit theory is developed to establish the asymptotic normality under such complex network dependent scenarios. We show that our method can achieve smaller asymptotic variance than existing methods when Xi is i.i.d. generated and estimated with empirical distribution, and provide theoretical guarantees for estimating the variance. Extensive numerical studies and a live-streaming data analysis are presented to illustrate the advantages of the proposed method.
报告人简介：
朱雪宁，复旦管理学院副教授，博士生导师。主要研究领域为网络数据分析、空间计量模型、高维数据建模等，研究成果发表于Journal of Econometrics, Annals of Statistics, Journal of the American Statistical Association等国际顶级期刊。2022年获国家自然科学基金优秀青年基金项目支持。担任全国工业统计学教学研究会青年统计学家协会副会长，中国现场统计研究会统计调查分会常务理事、STAT期刊编委等职务