courses.md

Causal Inference Courses

The following is a list of free courses in Causal Inference, sorted by format and date.

• Video Lectures
• Slides
• Notes

Video Lectures

• Causal Inference

  • Author: Kosuke Imai (Harvard University)
  • Year: 2022
  • Lectures: 11 x 0h:50 [Material]
  • Topics: PO, ATE, IV, RD, matching, IPW, FE, DiD, CATE

• Machine Learning and Causal Inference

  • Author: Susan Athey, Jann Spiess, and Stephan Wager (Stanford University)
  • Year: 2022
  • Lectures: 19 x 0h:30
  • Topics: ATE, CATE

• Modern Topics in Uncertainty Quantification

  • Author: Aaron Roth (University of Pennsylvania)
  • Year: 2022
  • Lectures: 12 x 2h:15

• Modern Sampling Methods: Design and Inference

  • Author: Keisuke Hirano (Yale University), Jack Porter (UW Madison)
  • Year: 2022
  • Lectures: 10 x 1h:15

• Causality Boot Camp

  • Author: Spencer Gordon (Caltech), Daniel Malinsky (Columbia University), Thomas Richardson (University of Washington), Chandler Squires (MIT)
  • Year: 2022
  • Lectures: 15 x 1h:05
  • Topics: GCM, Causal Discovery, PO, DAGs, Experimental Design

• Applied Methods

  • Author: Paul Goldsmith-Pinkham (Yale University)
  • Year: 2021
  • Lectures: 21 x 1h:00

• Causality

  • Author: Christina Heinze-Deml (ETH Zurich)
  • Year: 2021
  • Lectures: 12 x 2h:00

• Difference-in-Differences Reading Group

  • Author: multiple researchers
  • Year: 2021
  • Lectures: 9 x 1h:20

• Causal Inference with Panel Data

  • Author: Yiqing Xu (Stanford University)
  • Year: 2021
  • Lectures: 6 x 0h:50

• Econometrics

  • Author: Chris Conlon (New York University)
  • Year: 2020
  • Lectures: 32 x 0h:30

• Causal Inference

  • Author: Brady Neal (Quebec AI Institute)
  • Year: 2020
  • Lectures: 15 x 0h:45

• Mastering Mostly Harmless Econometrics

  • Author: Alberto Abadie, Joshua Angrist, and Christopher Walters (MIT)
  • Year: 2020
  • Lectures: 8 x 1h:15

• Machine Learning and Econometrics

  • Author: Susan Athey, Guido Imbens (Stanford University)
  • Year: 2018
  • Lectures: 9 x 1h:15

Slides

• Causal Machine Learning

  • Author: Max Farrell, Sanjog Misra (University of Chicago)
  • Year: 2023

• Causal Inference and Machine Learning

  • Author: Jens Hainmueller (Stanford University)
  • Year: 2023

• Applied Econometrics

  • Peter Hull (Brown University)
  • Year: 2023

• Causal Inference

  • Author: Fan Li (Duke University)
  • Year: 2022

• Causal Econometrics

  • Author: David Childers (Carnegie Mellon University)
  • Year: 2022

• Introduction to Causal Inference

  • Author: Maya L. Petersen, (University of California, Berkeley) Laura B. Balzer (UMass Amherst)
  • Year: 2022

Notes

• A First Course in Causal Inference

  • Author: Peng Ding (University of California, Berkeley)
  • Year: 2023

• R Guide for TMLE in Medical Research

  • Author: Ehsan Karim, Hanna Frank
  • Year: 2021

• Notes

  • Author: Aaporva Lal (Stanford University)

• Causal Inference

  • Author: Stefan Wager (Stanford University)

• Introduction to Modern Causal Inference

  • Author: Alejandro Schuler, Mark van der Laan (University of California, Berkeley)

• A User’s Guide to Statistical Inference and Regression

  • Author: Matthew Blackwell (Harvard University)

• Machine Learning for Econometrics

  • Author: Christophe Gaillac (Oxford University), Jérémy L'Hour (Capital Fund Management, CREST)