吃瓜51

为促进学科交流融合、拓宽师生学术视野、释放科研创新活力，助力人大数学学科走向一流，吃瓜51 设立“人大数学时间”，以专题研讨、高端学术论坛为载体，搭建数学思想充分碰撞、优秀人才不断涌流、创造活力竞相迸发的舞台。“人大数学时间”将持之以恒，久久为功，立志通过交流与创新、提出重大问题，引领数学学科及相关领域的创新与发展，成为对我国数学发展有贡献意义的平台。以下为“人大数学时间I”第五十五期信息：

议程：

6月26日（星期五）8:30

丁鹏教授报告及前沿问题探讨

地点：立德楼603

题目：Unifying regression-based and design-based causal inference in time-series experiments and crossover experiments

主讲专家：丁鹏，美国加利福尼亚大学伯克利分校统计系教授

摘要：

I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments.

Part I: Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, recent research has studied time-series experiments from the design-based perspective, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods, we examine the design-based properties of regression- based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Additionally, we show that asymptotically, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true, design-based variances. This part is based on //arxiv.org/pdf/2510.22864

Part II: Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However, standard analyses of crossover designs often rely on strong parametric models, making inference vulnerable to model misspecification. We unify the analysis of crossover designs using least squares, with restrictions on the coefficients and weights on the units. Based on the theory, we recommend specifying the regression function, weighting scheme, and coefficient restrictions to assess identifiability, construct efficient estimators, and estimate variances in a unified manner. This part is based on //arxiv.org/pdf/2511.09215

报告人简介：

丁鹏，2004-2011年就读于北京大学，获得数学学士、经济学学士以及统计学硕士学位，2015年于哈佛大学获得博士学位，后于哈佛大学公共卫生学院做博士后研究员。现为美国加州大学伯克利分校统计系教授。