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

Associations

In this section, we’ll demonstrate how to quantify associational relationships using conditional probability. Then, we’ll briefly introduce structural causal models. Finally, we’ll implement conditional probability queries using Python.

We already learned a lot about associations. We know that associations are related to observing and that they allow us to generate predictions. Let’s take a look at mathematical tools that will allow us to talk about associations in a more formal way.

We can view the mathematics of rung one from a couple of angles. In this section, we’ll focus on the perspective of conditional probability.

Conditional probability

Conditional probability is the probability of one event, given that another event has occurred. A mathematical symbol that we use to express conditional probability is | (known as a pipe or vertical bar). We read <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>P</mml:mi><mml:mo>(</mml:mo><mml:mi>X</mml:mi><mml:mo>|</mml:mo><mml:mi>Y</mml:mi><mml:mo>)</mml:mo></mml:math> as a probability of X given Y. This notation is a bit simplified (or...

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

Table of Contents (21) Chapters

Associations

Authors (1)

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

Table of Contents (21) Chapters

Associations

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Authors (1)

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