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Causal Inference and Discovery in Python

You're reading from  Causal Inference and Discovery in Python

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Pages 456 pages
Edition 1st Edition
Languages
Author (1):
Aleksander Molak Aleksander Molak
Profile icon Aleksander Molak

Table of Contents (21) Chapters

Preface 1. Part 1: Causality – an Introduction
2. Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? 3. Chapter 2: Judea Pearl and the Ladder of Causation 4. Chapter 3: Regression, Observations, and Interventions 5. Chapter 4: Graphical Models 6. Chapter 5: Forks, Chains, and Immoralities 7. Part 2: Causal Inference
8. Chapter 6: Nodes, Edges, and Statistical (In)dependence 9. Chapter 7: The Four-Step Process of Causal Inference 10. Chapter 8: Causal Models – Assumptions and Challenges 11. Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners 12. Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More 13. Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond 14. Part 3: Causal Discovery
15. Chapter 12: Can I Have a Causal Graph, Please? 16. Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications 17. Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond 18. Chapter 15: Epilogue 19. Index 20. Other Books You May Enjoy

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