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: Kelsey Ishimoto.

Each course module in the left panel will span several lectures. We will build this website over the course of the quarter, uploading lecture slides as we go.

Learning objectives

Syllabus

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) and the causal graphical model approach. 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. Finally, we will cover heterogeneous treatment effects, and causal machine learning approaches to causal heterogeneity.

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.

Pearl, J. 2009. Causality Cambridge: Cambridge University Press.

Office Hours

Falco: 3 hours per week (course, final project):

  • Monday: 3 to 4 pm at Boelter 5272 (no appointment needed).

  • Thursdays and Fridays: 3 to 4 pm via appoitnment: book here.

Kelsey: 3 hours a week (assignments, grading):

  • Wednesdays: 11:30 am to 12:30pm via Zoom (no appointment needed).

  • Tuesdays: 11am-12pm

  • Fridays: 11:30am - 12:30pm

Acknowledgments

Special thanks to Prof. Fabrizia Mealli and Prof. Davide Viviano for their invaluable help in preparing this course.