Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Length 456 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Aleksander Molak Aleksander Molak
Author Profile Icon Aleksander Molak
Aleksander Molak
Arrow right icon
View More author details
Toc

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

Graphs, graphs, graphs

This section will be a quick refresher on graphs and basic graph theory. If you’re not familiar with graphs – don’t worry – you can treat this section as a crash course on the topic.

Let’s start!

Graphs can be defined in multiple ways. You can think of them as discrete mathematical structures, abstract representations of real-world entities and relations between them, or computational data structures. What all of these perspectives have in common are the basic building blocks of graphs: nodes (also called vertices) and edges (links) that connect the nodes.

Types of graphs

We can divide graphs into types based on several attributes. Let’s discuss the ones that are the most relevant from the causal point of view.

Undirected versus directed

Directed graphs are graphs with directed edges, while undirected graphs have undirected edges. Figure 4.1 presents an example of a directed and undirected graph:

...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime