Description:

This course will cover the following basic topics:

- randomization inference in experiments: design and analysis

- observational studies: identification and estimation with and without unconfoundedness, sensitivity analysis

- causal mechanisms: post-treatment complications, interaction

- difference in differences and panel data

- spillover and peer effects

Prerequisite:

Calculus, linear algebra, probability, statistics

Reference:

Lecture notes from the instructor

A First Course in Causal Inference.pdf

Target Audience: 

Undergraduate students & Graduate students

Teaching Language: 

Chinese

Bio:

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.  

Registration：https://www.wjx.top/vm/riDZtV7.aspx# 

[Deadline: 12:00 (noon), May 30, 2023]