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 are counterfactuals?

Have you ever wondered where you would be today if you had chosen something different in your life? Moved to another city 10 years ago? Studied art? Dated another person? Taken a motorcycle trip in Hawaii? Answering these types of questions requires us to create alternative worlds, worlds that we have never observed. If you’ve ever tried doing this for yourself, you already know intuitively what counterfactuals are.

Let’s try to structure this intuition. We can think about counterfactuals as a minimal modification to a system (Pearl, Glymour, and Jewell, 2016). In this sense, they are similar to interventions. Nonetheless, there is a fundamental difference between the two.

Counterfactuals can be thought of as hypothetical or simulated interventions that assume a particular state of the world (note that interventions do not require any assumptions about the state of the world). For instance, answering a counterfactual question such as &...