Learning Resource: Causal Inference
Difference between Econometrics & Statistics
What is the difference between Econometrics and Statistics? Professor Joshua Angrist (MIT) explains the differnce in this video.
Causal Inference (theory) learning resources
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Golub Capital Social Impact Lab (2023). Machine Learning-based Causal Inference Tutorial.
- textbook
- YouTube tutorial
- Key idea to remember
- FWL Theorem ➜ Robinson’s Transformation ➜ R-learner (with R loss)
- See STATS 361: Causal Inference, page 36 for details.
- For R-learner, Nie, Xinkun, and Stefan Wager. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika provides a good summary.
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Peng Ding’s textbook A first course in causal inference.
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Wager, S. (2022). STATS 361: Causal Inference. Lecture notes, Stanford University.
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Mastering ‘Metrics, less theoretical.
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Causal Inference for The Brave and True is an open-source resource primarily focused on econometrics and the statistics of science.
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Causal Inference - Statistical Science - Duke University
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The slides are provided by Professor Fan Li.
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I highly recommend to read these slides as summary to get a big picture and read Peng Ding’s textbook for details and rigorous proofs.
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Introduction to Causal Inference (Fall 2020) by Brady Neal
- Brady kindly provides all his course material and YouTube tutorials. They are super helfpul!!!
Casual Inference in R
- R 📦 CRAN Task View: Causal Inference.
- Propensity Score Weighting tutorial: Generating inverse probability weights for both binary and continuous treatments. In this tutorial, the author introduces ipw and WeightIt 📦s.
- rlearner for Quasi-Oracle Estimation of Heterogeneous Treatment Effects.
- DoubleML — DoubleML documentation R 📦. Paper: DoubleML - An Object-Oriented Implementation of Double Machine Learning in R
- Causal Inference in R is a bookdown tutorial. It is new and incomplete.
- grf package for generalized random forests.
Casual Inerence in Python
- 🐍 EconML User Guide, this is for double machine learning.
