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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

Estimand first!

In this section, we’re going to introduce the notion of an estimand – an essential building block in the causal inference process.

We live in a world of estimators

In statistical inference and machine learning, we often talk about estimates and estimators. Estimates are basically our best guesses regarding some quantities of interest given (finite) data. Estimators are computational devices or procedures that allow us to map between a given (finite) data sample and an estimate of interest.

Let’s imagine you just got a new job. You’re interested in estimating how much time you’ll need to get from your home to your new office. You decide to record your commute times over 5 days. The data you obtain looks like this:

<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mrow><mrow><mo>[</mo><mn>22.1</mn><mo>,</mo><mn>23.7</mn><mo>,</mo><mn>25.2</mn><mo>,</mo><mn>20.0</mn><mo>,</mo><mn>21.8</mn><mo>]</mo></mrow></mrow></mrow></math>

One thing you can do is to take the arithmetic average of these numbers, which will give you the so-called sample mean - your estimate of the true average commute time. You might feel that this is not enough...

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