Awesome-Uplift-Model
How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？
Github项目地址：👉https://github.com/JackHCC/Awesome-Uplift-Model👈
👉https://github.com/JackHCC/Awesome-Uplift-Model👈
Basic Theory
Book Reading
- 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.
More Details
Code Examples
Courses
- 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
Tools
Probabilistic programming framework
Causal Structure Learning
Causal Inference
Datasets and Benchmark
Causal Inference
-
MIMIC II/III Data：ICU数据
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Advertisement Data：广告数据
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Geo experiment data：地理数据
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Economic data for Spanish regions：没有Ground Truth
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JustCause：Benchmark
Causal Discovery
- 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
Other Awesome List
- awesome-causality-algorithms
- awesome-causality-data
- awesome-causality
- Awesome-Causality-in-CV
- Awesome-Neural-Logic
- Awesome-Causal-Inference
How to Apply Causal ML to Real Scene Modeling？
Contact
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