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Causal Inference and Discovery in Python

You're reading from  Causal Inference and Discovery in Python

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781804612989
Pages 456 pages
Edition 1st Edition
Languages
Author (1):
Aleksander Molak Aleksander Molak
Profile icon Aleksander Molak

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? 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

What are interventions?

In this section, we’ll summarize what we’ve learned about interventions so far and introduce mathematical tools to describe them. Finally, we’ll use our newly acquired knowledge to implement an intervention example in Python.

The idea of intervention is very simple. We change one thing in the world and observe whether and how this change affects another thing in the world. This is the essence of scientific experiments. To describe interventions mathematically, we use a special <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>d</mml:mi><mml:mi>o</mml:mi></mml:math>-operator. We usually express it in mathematical notation in the following way:

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" display="block"><mml:mi>P</mml:mi><mml:mfenced separators="|"><mml:mrow><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>o</mml:mi><mml:mfenced separators="|"><mml:mrow><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:math>

The preceding formula states that the probability of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:math>, given that we set <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>X</mml:mi></mml:math> to 0. The fact that we need to change <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>X</mml:mi></mml:math>’s value is critical here, and it highlights the inherent difference between intervening and conditioning (conditioning is the operation that we used to obtain conditional probabilities in the previous section). Conditioning only modifies our view of the data...

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