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

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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

abduction 34

action recommendation systems 269

Active Bayesian Casual Inference (ABCI) 403

acyclic graph

versus cyclic graph 60, 61

adaptive hyper-box matching (AHB) 192

Additive Noise Model (ANM) 154, 359-363, 402

adjacency matrix 62-64, 342

Area Under the Uplift Curve (AUUC) 265

assignment operator 22

association 18, 20, 22

best practice 22-25

associational relationships 20

associative learning 7

assumptions 78, 81, 82

for causal discovery 80, 81

attention mechanism 304

augmented Lagrangian optimizer 384

availability heuristic 332

average treatment effect (ATE) 179, 227, 305, 397

versus average treatment effect on the control (ATC) 181, 182

average treatment effect on the control (ATC) 179

versus average treatment effect (ATE...

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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 (22) Chapters

Index

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Table of Contents (22) Chapters

Index

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