时 间：2023年12月15日 10:00-11:00
地 点: 普陀校区理科大楼A1314
报告人：石磊 加利福尼亚大学伯克利分校博士
主持人：王光辉 副教授
摘 要：
Neyman(1923/1990) introduced the randomization model, which contains the notation of potential outcomes to define causal effects and a framework for large-sample inference based on the design of the experiment. However, the existing theory for this framework is far from complete especially when the number of treatment levels diverges and the treatment group sizes vary. We provide a unified discussion of statistical inference under the randomization model with general treatment group sizes. We formulate the estimator in terms of a linear permutational statistic and use results based on Stein's method to derive various Berry--Esseen bounds on the linear and quadratic functions of the estimator. These new Berry--Esseen bounds serve as basis for design-based causal inference with possibly diverging treatment levels and a diverging number of causal parameters of interest. We also fill an important gap by proposing novel variance estimators for experiments with possibly many treatment levels without replications. Equipped with the newly developed results, design-based causal inference in general settings becomes more convenient with stronger theoretical guarantees.
报告人简介：
Lei Shi is a 4th-year Ph.D. student in Berkeley Biostatistics, advised by Professor Jingshen Wang and Professor Peng Ding. His research focuses on two threads. The first one is causal inference, especially design-based inference. The second one is statistical learning, with a focus on high-dimensional low rank matrix recovery. Before Berkeley, Lei obtained a B.S. degree in the School of Mathematical Sciences, Nankai University.