Recent advances in causal inference

Description:

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.

Prerequisite:

Basic Probability and Statistics

Reference:

https://arxiv.org/abs/2305.18793

Target Audience:

Undergraduate students, Graduate students

Teaching Language:

Chinese

About the Speaker 

Peng Ding 丁鹏

UC Berkeley

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.