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

Score-based causal discovery

In this section, we’ll introduce score-based methods for causal discovery. We’ll discuss the mechanics of the GES algorithm and implement it using gCastle.

Tabula rasa – starting fresh

The very first step of the PC algorithm was to build a fully-connected graph. GES starts on the other end of the spectrum, but first things first.

GES is a two-stage procedure. First, it generates the edges, then it prunes the graph.

The algorithm starts with a blank slate – an entirely disconnected graph – and iteratively adds new edges. At each step, it computes a score that expresses how well a new graph models the observed distribution, and at each step, the edge that leads to the highest score is added to the graph.

When no more improvement can be achieved, the pruning phase begins. In this phase, the algorithm removes edges iteratively and checks for score improvement. The phase continues until no further improvement...