A curated list of awesome Causal Inference resources.
The goal of this list is to serve a starting point for getting familiar with causality.
- The Book of Why by Judea Pearl, Dana Mackenzie
- Causal Inference Book (What If) by Miguel Hernán, James Robins FREE download
- Causal Inference in Statistics: A Primer by Judea Pearl, Madelyn Glymour, Nicholas P. Jewell
- Elements of Causal Inference: Foundations and Learning Algorithms by Jonas Peters, Dominik Janzing and Bernhard Schölkopf- FREE download
- Counterfactuals and Causal Inference: Methods and Principles for Social Research by Stephen L. Morgan, Christopher Winship
- Causal Inference Book by Hernán MA, Robins JM FREE download
- Causality: Models, Reasoning and Inference by Judea Pearl
- Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido W. Imbens and Donald B. Rubin
- Causal Inference: The Mixtape by Scott Cunningham FREE download
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A Crash Course in Causality: Inferring Causal Effects from Observational Data (Free)
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Causal ML Mini Course (Free)
- Lectures on Causality: 4 Parts by Jonas Peters
- Towards Causal Reinforcement Learning (CRL) - ICML'20 - Part I By Elias Bareinboim
- Towards Causal Reinforcement Learning (CRL) - ICML'20 - Part II By Elias Bareinboim
- On the Causal Foundations of AI By Elias Bareinboim
- Judea Pearl: Causal Reasoning, Counterfactuals, and the Path to AGI | Lex Fridman Podcast #56 By Judea Pearl and Lex Fridman
- NeurIPS 2018 Workshop on Causal Learning
- Causal Inference Bootcamp by Matt Masten
- DoWhy | Making causal inference easy (Python)
- Ananke: A module for causal inference (Python)
- Causal ML: A Package for Uplift Modeling and Causal Inference with ML (Python)
- CausalNex: A toolkit for causal reasoning with Bayesian Networks (Python)
- pgmpy: Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks