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

Personal experience and domain knowledge

We started this chapter by talking about how babies perform experiments to build causal world models. In this section, we’ll look into an adult’s approach to refining and building such models.

Imagine a rainy chilly afternoon somewhere in Northern Europe. You stand at a bus stop near your favorite park. There’s a large puddle on the street in front of you. You notice a car approaching from the left, driving close to the sidewalk. It seems that it will drive straight into the puddle. As the car approaches the puddle without slowing down, you instinctively jump behind the bus stop’s shelter. Water hits the shelter’s glass right next to your face, but fortunately, you are safe on the other side.

Your reaction was likely a result of many different factors, including a fast instinctive response to a threatening stimulus (splashing muddy water), but it was likely not entirely instinctive. You noticed a car...

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

Table of Contents (21) Chapters

Personal experience and domain knowledge

Authors (1)

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

Table of Contents (21) Chapters

Personal experience and domain knowledge

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Authors (1)

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