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

Full example

This section is here to help us solidify our newly acquired knowledge. We’ll run a full causal inference process once again, step by step. We’ll introduce some new exciting elements on the way and – finally – we’ll translate the whole process to the new GCM API. By the end of this section, you will have the confidence and skills to apply the four-step causal inference process to your own problems.

Figure 7.4 presents a graphical model that we’ll use in this section:

Figure 7.4 – A graphical model that we’ll use in this section

We’ll generate 1,000 observations from an SCM following the structure from Figure 7.4 and store them in a data frame:

SAMPLE_SIZE = 1000
S = np.random.random(SAMPLE_SIZE)
Q = 0.2*S + 0.67*np.random.random(SAMPLE_SIZE)
X = 0.14*Q + 0.4*np.random.random(SAMPLE_SIZE)
Y = 0.7*X + 0.11*Q + 0.32*S +
    0.24*np.random.random(SAMPLE_SIZE...