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] 

Notes：

Note Jun 5, 2023.pdf

Note Jun 7, 2023.pdf

Note Jun 12, 2023.pdf 

Note Jun 14, 2023.pdf

Note Jun 19, 2023.pdf

Note Jun 21, 2023.pdf

Note Jun 26, 2023.pdf

Note Jun 28, 2023.pdf

Note Jul 3, 2023.pdf

Note Jul 5, 2023.pdf

Note Jul 10, 2023.pdf

主分层july 12 and 17.pdf

Note+Jul+19,+2023.pdf

Note+Jul+24,+2023.pdf

Note+Jul+26,+2023.pdf