Welcome
This is the website for the graduate course in Causal Inference (Biostat M235) at the University of California, Los Angeles (UCLA).
Instructor: Falco J. Bargagli-Stoffi.
Teaching assistant: TBD
Each course module in the left panel will span several lectures. We will build this website over the course of the quarter, uploading course materials (e.g., finalized slides, assignments, additional readings) as we go.
Learning objectives
The course aims to introduce the key concepts and state-of-the-art methods for causal inference from randomized experiments and observational studies. We will first introduce the basic concepts of the potential outcome framework (with a particular focus on the essential role of the treatment assignment mechanism). Then, we will cover different situations corresponding to different assumptions concerning the assignment mechanism. We will discuss the design and analysis of experiments and how to make inference under different modes, including design (randomization)-based, frequentist, and Bayesian. We will cover the design and analysis of observational studies with regular assignment mechanisms where the unconfoundedness assumption is assumed to hold. We will introduce methods and sensitivity analyses to account for possible violations of unconfoundedness. We will cover the causal machine learning approaches for treatment effect heterogeneity, off-policy learning and bandits and adaptive experiments. Finally, if time permits, we will cover strategies for causal inference in the presence of non-randomized, endogenous treatments.
Teaching Material
Selected chapters from the book list below. Articles in (bio)statistical and econometric journals.
Imbens, G.W., and D.B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences Cambridge: Cambridge University Press.
Ding, P. 2023. A First Course in Causal Inference New York: Routledge.
Wager, S. 2025. Causal Inference: A Statistical Learning Approach Available online.
Office Hours
Falco: 3 hours per week (course, final project):
- Tuesday and Fridays: 5:00pm to 6:00pm (no appointment needed)
TA: 3 hours a week (assignments, grading):
- TBD
Acknowledgments
Special thanks to Prof. Fabrizia Mealli and Prof. Davide Viviano for their invaluable help in preparing this course. Special thanks to Kelsey Ishimoto for great teaching assistanship in the 2025 class.