Materials for PhD-level Causal Inference course (Pol Sci 200C, by Chad Hazlett) offered at UCLA in 2024 Spring, 2025 Spring, 2026 Spring. By Barney Chen.
- Probability, Linear Algebra, and Regression Review
- Potential Outcomes and Randomized Experiments
- Selection on Observables (SOO)
- Directed Acyclic Graphs (DAGs): Intro and Examples
- Sensitivity Analysis
- Difference-in-Differences (DiD)
- Instrumental Variables (IV)
- Regression Discontinuity Design (RDD)
(by Political Science Departments)
- Stat286/Gov2003: Causal Inference with Application (Harvard)
- Instructor(s): Kosuke Imai
- Slides: https://imai.fas.harvard.edu/teaching/cause.html
- Videos: https://www.youtube.com/@imaikosuke/playlists ⭐
- Gov2003: Causal Inference (Harvard)
- Instructor(s): Matthew Blackwell
- Slides: https://github.com/mattblackwell/gov2003-f21-site/tree/main/files
- PLSC 30600: Causal Inference (UChicago)
- POLISCI 450B: Political Methodology II (Stanford)
- Instructor(s): Apoorva Lal (TA for Jens Hainmueller)
- Slides: https://apoorvalal.github.io/talks/2021-GraduateSequenceTeaching
- Coding: https://apoorvalal.github.io/notebook/causal_inference_notes/
- POL-GA 1251: Quantitative Political Analysis II (NYU)
- Instructor(s): Cyrus Samii
- Slides: https://cyrussamii.com/?page_id=3893
- Lab Handouts: Spring 2021 Handouts
- POLI 784: Linear Methods in Causal Inference (UNC)
- Instructor(s): Ye Wang
- Slides: https://www.yewang-polisci.com/teaching
- 17.802 Quantitative Research Methods II (MIT)
- Instructor(s): F. Daniel Hidalgo
- Syllabus: https://www.dhidalgo.me/teaching
- A Basic Checklist for Observational Studies in Political Science by Yiqing Xu
(by Stats Departments)
- Stat 256: Causal Inference (UC Berkeley)
- Instructor(s): Peng Ding
- Notes: A First Course in Causal Inference [Python code][R code] ⭐
- STATS 361: Causal Inference (Stanford)
- Instructor(s): Stefan Wager
- Notes: https://web.stanford.edu/~swager/causal_inf_book.pdf
- STA 640: Causal Inference (Duke)
- Instructor(s): Fan Li
- Slides: https://www2.stat.duke.edu/~fl35/CausalInferenceClass.html
- Introduction to Causal Inference (online)
- Instructor(s): Brady Neal
- Slides + Videos: https://www.bradyneal.com/causal-inference-course
(by Econ Departments)
- Mixtape Sessions (online)
- Instructor(s): Scott Cunningham et al.
- Labs: https://github.com/Mixtape-Sessions ⭐
- A Comprehensive Course on DiD
- Instructor(s): Pedro H. C. Sant'Anna
- Slides: https://psantanna.com/did-resources ⭐
- ECON 574: Applied Empirical Methods (Yale)
- Instructor(s): Paul Goldsmith-Pinkham
- Slides: https://github.com/paulgp/applied-methods-phd
- Videos: https://www.youtube.com/playlist?list=PLWWcL1M3lLlojLTSVf2gGYQ_9TlPyPbiJ
- 47-873: Causal Econometrics (CMU)
- Instructor(s): David Childers
- Notes: https://donskerclass.github.io/CausalEconometrics.html
- ECON 2400: Applied Econometrics II (Brown)
- Instructor(s): Peter Hull
- Slides: https://about.peterhull.net/metrix
- Causal Machine Learning
- Instructor(s): Michael Knaus
- Slides: https://github.com/MCKnaus/causalML-teaching
- Difference-in-Differences Designs: A Practitioner's Guide - Baker et al. (2025)
- What’s Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature - Roth et al. (2023)
- A Practical Introduction to Regression Discontinuity Designs: Foundations & A Practical Introduction to Regression Discontinuity Designs: Extensions - Cattaneo et al. (2019; 2023)
- DiD literature - A Github repository that tracks current literature in DiD, with Notes
- Causal Models for Longitudinal and Panel Data: A Survey - Arkhangelsky and Imbens (2023)
- Recent Developments in Causal Inference and Machine Learning - Brand et al. (2023)