STAT 41530 Topics in Causal Inference

Description

In this course, we will have a brief introduction of both the potential outcome framework and the causal directed acyclic graph (DAG) for causal inference. We will discuss topics including confounding, instrumental variables (IV) and mediation analysis, with the applications of causal inference in genetics and epidemiological research.

We will follow the following book for the first 5 weeks:

Course Materials

WeekDateTopicSlidesExtra reading materials
12022-01-11Potential outcome framework: definition and randomized experimentsLecture 1
12022-01-13Potential outcome framework: observational studiesLecture 2
22022-01-18DAG: Markov assumption, d-seperation and connection with potential outcome frameworkLecture 3Pearl’s slides
22022-01-20DAG: do-operator, backdoor and frontdoor criteriaLecture 4Pearl’s slides