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 466 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Author (1):

Aleksander Molak

View More author details

Table of Contents (22) 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. Chapter 16: Unlock Your Book’s Exclusive Benefits

How to unlock these benefits in three easy steps

20. Index

21. Other Books You May Enjoy

Introduction to DoWhy and EconML

In this section, we’ll introduce the DoWhy and EconML packages.

We’ll start with an overview of the Python causal ecosystem and then discuss what DoWhy and EconML are.

Then, we’ll share why they are the packages of choice for this book.

Finally, we’ll dive deeper into DoWhy’s APIs and look into the integration between DoWhy and EconML.

Yalla!

Python causal ecosystem

The Python causal ecosystem is dynamically expanding. It is becoming increasingly rich and powerful. At the same time, it can also be confusing, especially when you’re just starting your causal journey.

The following list presents a selection of actively developed Python causal packages that I am aware of at the time of writing this book:

CATENets: A package implementing a number of neural network-based conditional average treatment effect estimators in JAX and PyTorch. We introduce CATENets in Chapter 11: https://github...