How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？
- 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
- Causal Inference for Data Science by Aleix Ruiz de Villa
The most commonly used models for causal inference are Rubin Causal Model (RCM; Rubin 1978) and Causal Diagram (Pearl 1995). Pearl (2000) introduced the equivalence of these two models, but in terms of application, RCM is more accurate, while Causal Diagram is more intuitive, which is highly praised by computer experts.
Donald Bruce Rubin (born December 22, 1943) is an Emeritus Professor of Statistics at Harvard University. He is most well known for the Rubin causal model, a set of methods designed for causal inference with observational data, and for his methods for dealing with missing data.
Judea Pearl (born September 4, 1936) is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on belief propagation).
- The book of why: The new science of cause and effect by Judea Pearl and Dana Mackenzie, 2018. Get Book [Must Read] An amazing beginner's guide to graph-based causality models.
- Causal inference in statistics: A primer by Madelyn Glymour, Judea Pearl, Nicholas P Jewell, 2016. Get Book [Must Read] The essense of causal graph, adjustment, and counterfactuals in FOUR easy-to-follow chapters.
- Causality: Models, Reasoning, and Inference by Judea Pearl, 2009. Get Book [Suggested] A formal and comprehensive discussion of every corner of Pearl's causality.
- Causal inference in statistics, social, and biomedical sciences Guido W Imbens, Donald B Rubin, 2015. Get Book [Must Read] A formal and comprehensive discussion of Rubin's potential outcome framework.
- Causal Inference for The Brave and True Matheus Facure, 2021. Get Book [Must Read] A new book that describes causality in an amazing mixture of Pearl's and Rubin's frameworks.
Not necessarily books. Posts and papers are included.
- Resolving disputes between J. Pearl and D. Rubin on causal inference [Go to post] [Must Read] The post from Prof. Gelman shows the disputes from Rubin's perspective. It helps understand why Pearl's framework faces great challenges in the statistic community while being so successful in machine learning and social computing.
- “The Book of Why” by Pearl and Mackenzie [Go to post] [Must Read] Critics from Rubin's causal perspective to the famous guiding book for causality: The book of why.
- Chapter 8, The Book of Why? [Get book] [Must Read] Pearl's overall discussion of the short comings of Rubin's potential outcome framework.
- Can causal inference be done in statistical vocabulary? [Go to post] [Must Read] Pearl's initial reponse to Gelman's critics on The book of why.
- More on Gelman’s views of causal inference [Go to post] [Must Read] Pearl's next reponse to Gelman's critics on The book of why.
- Introduction to Causal Inference (Fall2020) (Free)
- A Crash Course in Causality: Inferring Causal Effects from Observational Data (Free)
- Causal Inference with R - Introduction (Free)
- 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
- Partial Dependence Plot (PDP)；
- Individual Conditional Expectation (ICE)
- Permuted Feature Importance
- Global Surrogate
- Local Surrogate (LIME)
- Shapley Value (SHAP)
- MIMIC II/III Data：ICU数据
- Advertisement Data：广告数据
- Geo experiment data：地理数据
- Economic data for Spanish regions：没有Ground Truth
- California’s Tobacco Control Program：
- Air Quality Data：
- Monetary Policy Data：
- JustCause：Benchmark
- Causal Inference for Time series Analysis: Problems, Methods and Evaluation
- Causeme：Benchmark
- Real Dataset：
- US Manufacturing Growth Data
- Diabetes Dataset
- Temperature Ozone Data
- OHDNOAA Dataset
- Neural activity Dataset
- Human Motion Capture
- Traffic Prediction Dataset
- Stock Indices Data
- Composite Dataset：
- Confounding/ Common-cause Models
- Non-Linear Models
- Dynamic Models
- Chaotic Models
- awesome-causality-algorithms
- awesome-causality-data
- awesome-causality
- Awesome-Causality-in-CV
- Awesome-Neural-Logic
- Awesome-Causal-Inference
- Basic Conception
- ATE Method
- Basic of Uplift
- Uplift Modeling
- Evaluation of Uplift
- Debias
- Causal ML Framework
- Uplift Paper Reading
- Causal Discovery Paper Reading
- TARNet
- DragonNet
- DUNet
- CEVAE
- Tree Models
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