You're reading from Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804612989

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Author (1):

Aleksander Molak

View More author details

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? 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

What is a graphical model?

In this section, we’re going to discuss what graphical causal models (GCMs) are and how they can help in causal inference and discovery.

GCMs can be seen as a useful framework that integrates probabilistic, structural, and graphical aspects of causal inference.

Formally speaking, we can define a graphical causal model as a set consisting of a graph and a set of functions that induce a joint distribution over the variables in the model (Peters et al., 2017).

The basic building blocks of GCM graphs are the same as the basic elements of any directed graph: nodes and directed edges. In a GCM, each node is associated with a variable.

Importantly, in GCMs, edges have a strictly causal interpretation, so that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi><mml:mo>→</mml:mo><mml:mi>B</mml:mi></mml:math> means that causes (this is one of the differentiating factors between causal models and Bayesian networks; Pearl & Mackenzie, 2019, pp. 111-113). GCMs are very powerful because certain combinations of nodes and edges can reveal important...