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

Concepts

Data Science

Author (1):

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

Gradient-based causal discovery

In this section, we’ll introduce gradient-based causal discovery methods. We’ll discuss the main contributions of this family of methods and their main disadvantages. Finally, we’ll implement selected methods using gCastle and compare their performance with other families.

What exactly is so gradient about you?

2018 was an exciting year for the causal discovery community. Xun Zheng from CMU and his colleagues presented an interesting paper during the 2018 NeurIPS conference.

The work was titled DAGs with NO TEARS: Continuous Optimization for Structure Learning and introduced a novel approach to causal structure learning (though we need to say that the authors did not explicitly state that their method is causal).

The proposed method (called NOTEARS) was not based on a set of independence tests or local heuristics but rather treated the task of structure learning as a joint, continuously-optimized task.

One of the main...