Causal Inference and Discovery in Python — Chapters

• Book Summary

Chapters

• Chapter 1: 第01章：Causality – Hey, We Have Machine Learning, So Why Even Bother?
• Chapter 2: 第02章：Judea Pearl and the Ladder of Causation
• Chapter 3: 第03章：Regression, Observations, and Interventions
• Chapter 4: 第04章：Graphical Models
• Chapter 5: 第05章：Forks, Chains, and Immoralities
• Chapter 6: 第06章：Nodes, Edges, and Statistical (In)dependence
• Chapter 7: 第07章：The Four-Step Process of Causal Inference
• Chapter 8: 第08章：Causal Models – Assumptions and Challenges
• Chapter 9: 第09章：Causal Inference and Machine Learning – from Matching to Meta-Learners
• Chapter 10: 第10章：Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
• Chapter 11: 第11章：Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
• Chapter 12: 第12章：Can I Have a Causal Graph, Please?
• Chapter 13: 第13章：Causal Discovery and Machine Learning – from Assumptions to Applications
• Chapter 14: 第14章：Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond
• Chapter 15: 第15章：Epilogue