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 learned a lot in this chapter, and you deserve some serious applause for coming this far!

In this chapter, we learned a lot. We started with the notion of d-separation. Then, we showed how d-separation is linked to the idea of an estimand. We discussed what causal estimands are and what their role is in the causal inference process.

Next, we discussed two powerful methods of causal effect identification, the back-door and front-door criteria, and applied them to our ice cream and GPS usage examples.

Finally, we presented a generalization of front-door and back-door criteria, the powerful framework of do-calculus, and introduced a family of methods called instrumental variables, which can help us identify causal effects where other methods fail.

The set of methods we learned in this chapter gives us a powerful causal toolbox that we can apply to real-world problems.

In the next chapter, we’ll demonstrate how to properly structure an end-to-end...