Causality Resources
A curated collection of resources on causal inference and causal discovery that I've found useful — talks, lectures, books, websites and papers.
Websites
- https://causalinference.gitlab.io contains courses and a book on causality.
- https://matheusfacure.github.io/python-causality-handbook/landing-page.html simplified introduction to acusal inference with python examples.
- http://philsci-archive.pitt.edu/19773/1/Markus_Commentary_Revisions_Unblinded.pdf comparison of Rubin's and Pearl's causal modelling frameworks.
- https://github.com/vveitch/causality-tutorials useful code examples of estimating causal quantities.
Talks
- Differentiable causal discovery.
- Talk about differentiable causal discovery
- Causal representation learning
- Domain adaptation by causal inference
- Leon Bottou
- Causal data science tutorial
- Efficient neural causal discovery
- Causal inference with bayes rule
- Bernhard Schoelkopf, learning causal mechanisms
Lectures
- Jonas Peters mini-course on causality
- Nick Huntington Klein intro to causality
- Brady Neal introduction to causal inference **recommend**
Free Books
- A Concise Course in Statistical Inference Has a chapter on causal inference, but it's a great all-round book for statistics
- Causal Inference: What If Fundamentals of causal inference, weak connection to machine learning
- Elements of Causal Inference To me personally, this book under-delivers in the sense that it's not a textbook and doesn't build intuition very well, but rather a collection of the authors papers, albeit it does speak more about the connection of causality to ML.
- Introduction to Causal Inference Very good book that covers the very basics while building intuition, it also covers causal discovery from observational data.
- Counterfactuals and Causal Inference
- Causal Inference: The Mixtape has useful exercises in R and python, also covers potential outcomes and instrumental variables pretty well