Materials for PhD-level Causal Inference course (Pol Sci 200C, by Chad Hazlett) offered at UCLA in 2024 Spring, 2025 Spring. By Barney Chen.

TA Section: Every Friday 9:00-9:50 on Zoom, using this link. I hold office hours after the TA section, from 10:00 to 12:00.

• 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)
  • Classical 2x2 DiD, TWFE, Synthetic Control
  • Recent Advancements in Generalized 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)
  • Slides + Codes: https://github.com/UChicago-pol-methods/plsc-30600-causal-inference
• 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)