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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 456 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 (21) 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. Index 20. Other Books You May Enjoy

Should we always control for all available covariates?

Multiple regression provides scientists and analysts with a tool to perform statistical control – a procedure to remove unwanted influence from certain variables in the model. In this section, we’ll discuss different perspectives on statistical control and build an intuition as to why statistical control can easily lead us astray.

Let’s start with an example. When studying predictors of dyslexia, you might be interested in understanding whether parents smoking influences the risk of dyslexia in their children. In your model, you might want to control for parental education. Parental education might affect how much attention parents devote to their children’s reading and writing, and this in turn can impact children’s skills and other characteristics. At the same time, education level might decrease the probability of smoking, potentially leading to confounding. But how do we actually know whether...

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