Here I have collected various resources on causality that I found useful. This includes talks, books, lecture, websites series and occasionally papers
https://www.youtube.com/watch?v=taUMuf2Fno8 - Differentiable causal discovery.
https://www.youtube.com/watch?v=taUMuf2Fno8 - Talk about differentiable causal discovery
https://www.youtube.com/watch?v=hKs-PHZnIbQ - Causal representation learning
https://www.youtube.com/watch?v=z748Lf4QTlE - Domain adaptation by causal inference
https://www.youtube.com/watch?v=Rewr4GmkYEk - Leon Bottou
https://www.youtube.com/watch?v=2zkQjhMiZq4 - Causal data science tutorial
https://www.youtube.com/watch?v=ro_5FqiS5qU - Efficient neural causal discovery
https://www.youtube.com/watch?v=VoJWIKEF4x8 - Causal inference with bayes rule
https://www.youtube.com/watch?v=4qc28RA7HLQ&t=905s - Bernhard Schoelkopf, learning causal mechanisms
https://www.youtube.com/watch?v=zvrcyqcN9Wo - Jonas Peters mini-course on causality
https://nickchk.com/econ305.html - Nick Huntington Klein intro to causality
https://www.bradyneal.com/causal-inference-course - Brady Neal introduction to causal inference recommend
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.
Causal Inference: The Mixtape - has useful exercises in R and python, also covers potential outcomes and instrumental variables pretty well