The following is a list of free courses in Causal Inference, sorted by format and date.
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- Author: Kosuke Imai (Harvard University)
- Year: 2022
- Lectures: 11 x 0h:50
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Machine Learning and Causal Inference
- Author: Susan Athey, Jann Spiess, and Stephan Wager (Stanford University)
- Year: 2022
- Lectures: 19 x 0h:30
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Modern Topics in Uncertainty Quantification
- Author: Aaron Roth (University of Pennsylvania)
- Year: 2022
- Lectures: 12 x 2h:15
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Modern Sampling Methods: Design and Inference
- Author: Keisuke Hirano (Yale University), Jack Porter (UW Madison)
- Year: 2022
- Lectures: 10 x 1h:15
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- Author: various professors at the Simons’ Institute (Berkeley).
- Year: 2022
- Lectures: 15 x 1h:05
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- Author: Paul Goldsmith-Pinkham (Yale University)
- Year: 2021
- Lectures: 21 x 1h:00
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Difference-in-Differences Reading Group
- Author: multiple researchers
- Year: 2021
- Lectures: 9 x 1h:20
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Causal Inference with Panel Data
- Author: Yiqing Xu (Stanford University)
- Year: 2021
- Lectures: 6 x 0h:50
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- Author: Chris Conlon (New York University)
- Year: 2020
- Lectures: 32 x 0h:30
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- Author: Brady Neal (Quebec AI Institute)
- Year: 2020
- Lectures: 15 x 0h:45
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Mastering Mostly Harmless Econometrics
- Author: Alberto Abadie, Joshua Angrist, and Christopher Walters (MIT)
- Year: 2020
- Lectures: 8 x 1h:15
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Machine Learning and Econometrics
- Author: Susan Athey, Guido Imbens (Stanford University)
- Year: 2018
- Lectures: 9 x 1h:15
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Causal Inference and Machine Learning - Aaporva Lal (2023)
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Applied Econometrics - Peter Hull (2023)
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Causal Inference - Fan Li (2022)
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Causal Econometrics - David Childers (2022)
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A First Course in Causal Inference - Peng Ding (2023)
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Notes - Aaporva Lal (2023)
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Causal Inference - Stefan Wager (2020)
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Introduction to Modern Causal Inference - Alejandro Schuler, Mark van der Laan (20??)