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

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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

To get the most out of this book

The code for this book is provided in the form of Jupyter notebooks. To run the notebooks, you’ll need to install the required packages.

The easiest way to install them is using Conda. Conda is a great package manager for Python. If you don’t have Conda installed on your system, the installation instructions can be found here: https://bit.ly/InstallConda.

Note that Conda’s license might have some restrictions for commercial use. After installing Conda, follow the environment installation instructions in the book’s repository README.md file (https://bit.ly/InstallEnvironments).

If you want to recreate some of the plots from the book, you might need to additionally install Graphviz. For GPU acceleration, CUDA drivers might be needed. Instructions and requirements for Graphviz and CUDA are available in the same README.md file in the repository (https://bit.ly/InstallEnvironments).

The code for this book has been only tested on Windows 11 (64-bit).

Software/hardware covered in the book	Operating system requirements
Python 3.9	Windows, macOS, or Linux
DoWhy 0.8	Windows, macOS, or Linux
EconML 0.12.0	Windows, macOS, or Linux
CATENets 0.2.3	Windows, macOS, or Linux
gCastle 1.0.3	Windows, macOS, or Linux
Causica 0.2.0	Windows, macOS, or Linux
Causal-learn 0.1.3.3	Windows, macOS, or Linux
Transformers 4.24.0	Windows, macOS, or Linux

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Table of Contents (21) Chapters

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