新葡京官方

时间：2025年12月22日10:00-11:30（周一）

地点：明德主楼734

主讲人：丁鹏 加州大学伯克利分校副教授

主持人：马骏 新葡京官方 副教授

主题：Design-based causal inference with network interference

讲座简介：

I will report my recent research in design-based causal inference with network interference.(1) Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments.（//www.sciencedirect.com/science/article/abs/pii/S0304407625001733）(2) Bipartite experiments arise in various fields, in which the treatments are randomized over one set of units, while the outcomes are measured over another separate set of units. However, existing methods often rely on strong model assumptions about the data-generating process. Under the potential outcomes formulation, we explore design-based causal inference in bipartite experiments under weak assumptions by leveraging the sparsity structure of the bipartite graph that connects the treatment units and outcome units.（//arxiv.org/abs/2501.09844）

主讲人简介：

丁鹏，现任加州大学伯克利分校副教授，研究领域包括因果推断理论与应用、随机试验的设计与分析，现任统计学顶级期刊中的Journal of the Statistical Association与Biometrika副主编，他大量论文发表在统计学顶级期刊Annals of Statistics，Journal of the American Statistical Association，Journal of the Royal Statistical Society, Series B，Biometrika。他在计量经济学领域的研究发表在Econometrica和Journal of Econometrics。