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

Part 1: Causality – an Introduction

Part 1 of this book will equip us with a set of tools necessary to understand and tackle the challenges of causal inference and causal discovery.

We’ll learn about the differences between observational, interventional, and counterfactual queries and distributions. We’ll demonstrate connections between linear regression, graphs, and causal models.

Finally, we’ll learn about the important properties of graphical structures that play an essential role in almost any causal endeavor.

This part comprises the following chapters:

Chapter 1, Causality – Hey, We Have Machine Learning, So Why Even Bother?
Chapter 2, Judea Pearl and the Ladder of Causation
Chapter 3, Regression, Observations, and Interventions
Chapter 4, Graphical Models
Chapter 5, Forks, Chains, and Immoralities

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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

Part 1: Causality – an Introduction

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

Part 1: Causality – an Introduction

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Authors (1)

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