STAT 41530 Topics in Causal Inference


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
32022-01-25DAG: selection bias. ReviewLecture 5
32022-01-27Estimation and statistical inferenceLecture 6Dr. Fan Li’s slides
42022-02-01Instrumental VariablesLecture 7
42022-02-03Causal mediation analysisLecture 8Tyler’s causal mediation lecture
42022-02-08Lord paradoxLecture 9Cox and McCullagh 1982, Holland and Rubin 1983, Pearl 2016
42022-02-10Comparisons between potential outcome and DAGLecture 10Imbens' paper, Pearl’s feedback on the paper