haotianchen/Causal-Inference

TA Section notes for Pol Sci 200C by Barney Chen

Causal Inference Notes

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

Problem set 4 is due by June 9! Please submit a PDF!

TA Section Notes

• Math and Regression Review
  • Basic Probability Theory and Basic Linear Algebra
  • OLS in Matrix Form and Standard Errors in OLS
• Potential Outcomes and Randomized Experiments
• Selection on Observables (SOO)
• Sensitivity Analysis
• Directed Acyclic Graphs (DAGs): Intro and Examples
• Difference-in-Differences (DiD)
• Instrumental Variables (IV)
• Regression Discontinuity Design (RDD)

Resources

Other Courses

• Stat286/Gov2003: Causal Inference with Application (Harvard)

  • Instructor(s): Kosuke Imai
  • Videos: https://www.youtube.com/@imaikosuke/playlists
  • Slides: https://imai.fas.harvard.edu/teaching/cause.html

• Gov2003: Causal Inference (Harvard)

  • Instructor(s): Matthew Blackwell
  • Slides: https://github.com/mattblackwell/gov2003-f21-site/tree/main/files

• 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

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• Introduction to Causal Inference (online)

  • Instructor(s): Brady Neal
  • Slides + Videos: https://www.bradyneal.com/causal-inference-course

• Stat 256: Causal Inference (UC Berkeley)

  • Instructor(s): Peng Ding
  • Notes: A First Course in Causal Inference [Python code][R code]

• STA 640: Causal Inference (Duke)

  • Instructor(s): Fan Li
  • Slides: https://www2.stat.duke.edu/~fl35/CausalInferenceClass.html

• STATS 361: Causal Inference (Stanford)

  • Instructor(s): Stefan Wager
  • Notes: https://web.stanford.edu/~swager/stats361.pdf

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• 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

Current Literature

• 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)
• 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)