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Causal Inference and Discovery in Python

You're reading from   Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Length 456 pages
Edition 1st Edition
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Author (1):
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Aleksander Molak Aleksander Molak
Author Profile Icon Aleksander Molak
Aleksander Molak
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Causality – an Introduction
2. Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? FREE CHAPTER 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

Constraint-based causal discovery

In this section, we’ll introduce the first of the four families of causal discovery methods – constraint-based methods. We will learn the core principles behind constraint-based causal discovery and implement the PC algorithm (Sprites et al., 2000).

By the end of this chapter, you will have a solid understanding of how constraint-based methods work and you’ll know how to implement the PC algorithm in practice using gCastle.

Constraints and independence

Constraint-based methods (also known as independence-based methods) aim at decoding causal structure from the data by leveraging the independence structure between three basic graphical structures: chains, forks, and colliders.

Let’s start with a brief refresher on chains, forks, and colliders. Figure 13.4 presents the three structures:

Figure 13.4 – The three basic graphical structures

Figure 13.4 – The three basic graphical structures

In Chapter 5, we demonstrated that the...

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