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

Wrapping it up

We started this chapter by refreshing our knowledge of graphs and learned how to build simple graphs using Python and the NetworkX library. We introduced GCMs and DAGs and discussed some common limitations and challenges that we might face when using them.

Finally, we examined selected approaches to model causal systems with cycles.

Now you have the ability to translate between the visual representation of a graph and an adjacency matrix. The basic DAG toolkit that we’ve discussed in this chapter will allow you to work smoothly with many causal inference and causal discovery tools and will help you represent your own problems as graphs, which can bring a lot of clarity – even in your work with traditional (non-causal) machine learning.

The knowledge you gained in this chapter will be critical to understanding the next chapter and the next two parts of this book. Feel free to review this chapter anytime you need.

In the next chapter, we’...