Recent advances in causal inference

Speaker:Peng Ding (UC Berkeley)
Schedule:Mon. & Wed., 19:20-20:50, from Dec. 4, 2023 to Dec. 13, 2023
Venue:(Only on-site) Lecture Hall C548, Tsinghua University Shuangqing Complex Building A(清华大学双清综合楼A座C548报告厅)


I will give four lectures based on the following papers.

Gao, M. and Ding. P. (2023+). Causal inference in network experiments: regression-based analysis and design-based properties.

Lu, S. and Ding, P. (2023+). Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding.

Lu, S., Jiang, Z. and Ding, P. (2023+) Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation.

Shi, L. and Ding, P. (2022+). Berry-Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes.


Basic Probability and Statistics


Target Audience: Undergraduate students, Graduate students

Teaching Language: Chinese



Peng Ding is an Associate Professor in the Department of Statistics, UC Berkeley, working on causal inference. He obtained his Ph.D. from the Department of Statistics and worked as a postdoctoral researcher in the Department of Epidemiology, both at Harvard.