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

Extra – going beyond observations

In certain cases, we might be able to intervene on some or all variables in order to facilitate or improve the results of a causal discovery process.

In this short section, we’ll introduce two methods that can help us make sure that we make good use of such interventions.

ENCO

Efficient Neural Causal Discovery (ENCO; Lippe et al., 2022) is a causal discovery method for observational and interventional data. It uses continuous optimization and – as we mentioned earlier in the section on DECI – parametrizes edge existence and its orientation separately. ENCO is guaranteed to converge to a correct DAG if interventions on all variables are available, but it also performs reasonably well on partial intervention sets. Moreover, the model works with discrete, continuous, and mixed variables and can be extended to work with hidden confounding. The model code is available on GitHub (https://bit.ly/EncoGitHub).

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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

Extra – going beyond observations

ENCO

ABCI

Authors (1)

Personalised recommendations for you

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

Extra – going beyond observations

ENCO

ABCI

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you