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Causal Inference and Discovery in Python

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

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Length 466 pages
Edition 1st Edition
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Author (1):
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Aleksander Molak Aleksander Molak
Author Profile Icon Aleksander Molak
Aleksander Molak
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Table of Contents (22) Chapters Close

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 20. Index 21. Other Books You May Enjoy

Causality and business

In this section, we’ll describe a couple of real-world use cases where causal systems have been successfully implemented to address business challenges and discuss how causality intersects with business frameworks.

How causal doers go from vision to implementation

Geminos is a US-based company, with a mission to help businesses across industries solve their challenges using causality. One of Geminos’ success stories that I particularly like comes from their engagement with an industrial manufacturer of metal products; let’s call them Company M.

I like this story, because it emphasizes the broad applicability of causality that goes way beyond the most popular cases in marketing and econometrics.

Let’s see how it worked.

Company M was interested in optimizing the process of production of one of its products. They formulated four main questions:

  1. How do we minimize an important characteristic of product P?
  2. Which...
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