In many applications of statistics, a large proportion of the questions of interest are about causality rather than questions of description or association. Would booster shots reduce the chance of getting infected by the new variant of COVID-19? How does a new tax policy affect the economic activity? Can a universal health insurance program improve people’s health? In this course, we will introduce some basic concepts and methods in causal inference and discuss examples from various disciplines. The course plans to cover the potential outcome framework, randomize experiments, randomization and model-based inference, matching, sensitivity analysis, and instrumental variables. Examples include the evaluation of job training programs, educational voucher schemes, clinical trials and observational data of medical treatments, smoking, the influenza vaccination study, and more.


Course Materials

WeekDateTopicSlidesExtra materialsDue
12022-03-29Introduction with four examplesLecture 1Papers listed at the end of slides
12022-03-31Potential outcome framework conceptsLecture 2Papers listed at the end of slides
22022-04-05Randomized experiment and Fisher’s exact p-valueLecture 3
22022-04-07Case study with Fisher’s exact p-valueLecture 4
32022-04-12Neyman’s repeated sampling approachLecture 5R Example to compute Fisher’s exact p-value
32022-04-13HW1 due at 11:59pm
32022-04-14Regression for randomized experimentsLecture 6
42022-04-19Statified randomized experimentsLecture 7R Example for regression
42022-04-21Pairwise randomized experimentsLecture 8R Example for analyzing pairwise experiment
52022-04-25HW2 due at 11:59pm
52022-04-26Two case studies, non-compliance in randomized experimentsLecture 9R Example for case study,
52022-04-28Non-compliance in randomized experimentsLecture 10
62022-05-03Unconfoundedness, conditional randomized experimentsLecture 11
62022-05-05Observational studies, propensity score estimationLecture 12
62022-05-08HW3 due at 11:59pm
72022-05-10Matching methodsLecture 13
72022-05-12Matching case study, IPW, doubly robust estimatorLecture 14R Example for matching
82022-05-17IPW, trimming and subclassificationLecture 15R Example for weighting
82022-05-19Assess unconfoundedness, sensitivity analysisLecture 16R Example for subclassification
82022-05-22HW4 due at 11:59pm
92022-05-27Final project due at 11:59pm (release on 2022/5/20)