Teaching
I teach graduate courses in causal inference and machine learning for the social sciences at UCLA, and co-lead the Practical Causal Inference lab.
Current courses (Spring 2026)
PS 200C — Causal Inference
Graduate course for political science PhD students. Covers identification, estimation, and inference for causal effects: potential outcomes, matching and weighting, instrumental variables, regression discontinuity, difference-in-differences, synthetic controls, sensitivity analysis.
Stat 256 — Causality
Graduate statistics course on causality: foundations of causal inference, identification under ignorability, instruments, sensitivity, modern estimation (doubly robust methods, machine-learning-based approaches), and open problems at the methodological frontier.
Broader causal inference resources
The Practical Causal Inference lab site, which I co-direct with Onyebuchi Arah, hosts:
- getting-started guides aimed at applied researchers,
- short courses and tutorials on specific methods (sensitivity, balancing, kernel methods, etc.),
- a directory of software and companion materials.
If you’re a researcher looking to adopt causal methods in your own work without committing to a full graduate course, that’s the best starting point.
Past courses
A full list of past teaching is on my CV. Selected course materials from recent years are on GitHub.