本文介绍了因果机器学习的基础理论，推荐了多本必读的因果推断书籍，并提供了资源链接。涵盖了Pearl的结构因果模型与Rubin的潜在结果模型两大主流方法论，以及它们之间的争议与融合。
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.
Donald Bruce Rubin (born December 22, 1943) is an Emeritus Professor of Statistics at Harvard University. He is
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