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

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 31

action recommendation systems 261

Active Bayesian Causal Inference (ABCI) 393

acyclic graph

versus cyclic graph 56, 57

adaptive hyper-box matching (AHB) 186

Additive Noise Model (ANM) 148, 349-353, 392

adjacency matrix 58-60, 332

Area Under the Uplift Curve (AUUC) 257

assignment operator 20

association 16, 18, 20

best practice 20-23

associational relationships 18

associative learning 5

assumptions 74, 77, 78

for causal discovery 76, 77

attention mechanism 295

augmented Lagrangian optimizer 374

availability heuristic 322

average treatment effect (ATE) 173, 219, 296, 387

versus average treatment effect on the control (ATC) 175

average treatment effect on the control (ATC) 173

versus average treatment effect (ATE) 175...

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Table of Contents (21) Chapters