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Recent advances in causal inference

来源: 12-04

时间:Mon. & Wed., 19:20-20:50, from Dec. 4, 2023 to Dec. 13, 2023

地点:(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)

主讲人:Peng Ding 丁鹏(UC Berkeley)

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.


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