 |
| [image source: Amazon.com] |
Causal Inference and Discovery with Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
https://www.amazon.com/Causal-Inference-Discovery-Python-learning/dp/1804612987
- https://causalpython.io/
- Author: Aleksander Molak
- https://www.amazon.com/stores/Aleksander-Molak/author/B0C6QVQJCY
- https://www.linkedin.com/in/aleksandermolak/
- Lespire Consulting, Co-founder & Consultant
- https://lespire.io/
- Corporate AI trainings for effective teams
- Pages: 465 pages
- Publication Date: May 31, 2023
- Github Repository: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python
[My Amazon posted review: link]
My Review Title:
A Fascinating Book on Causal Machine Learning - and the best Packt published book I’ve ever read
My Review Summary:
Why should you want to read this book?
Understand the landscape of causality modeling – and a broad survey of the many available tools – as well as the historical background - and fairly recent trends - in causality modeling research.
To learn the reason to use causal modeling rather than traditional machine learning.
Understand the libraries and algorithms available for causal modeling.
Understand the use cases (and limitations) of different causal modeling strategies and capabilities.
Understand the importance (and challenges) of obtaining Expert Knowledge for causal modeling.
Understand the practical aspects of applying and implementing causality modeling to business problems.
This book will save you months of effort trying to research and assemble the equivalent information. The book offers an optimal and efficient path to learning the information.
What I particularly liked:
- Breadth and depth of the treatment of the material.
- Inclusion of interesting examples – written in Python.
- Use of Jupyter notebooks
- The excellent quality of the images for source code and graphics.
- Inclusion of the link to the companion github repository for the book
- QR code to download a PDF version of the book
- Inclusion of a References section at the end of chapters, with suggested additional reading. This *really* enriches a book for me – and indicates that the author has given deep thought to how to expand and elevate the reader’s understanding of the material.
- The treatment and depth of the material covered – is exceptionally well-done.
- The writing is ACCESSIBLE. The author invests sufficient effort to bring the reader along – and build their knowledge with successive layering of concepts.
- The material covered is fascinating - well-researched - and every single chapter is filled with rich content - as well as extensive citations in the References section, at the end of each chapter, with *numerous* high quality suggestions for additional reading.
- The inclusion of excellent coding examples.
- The writing is exceptionally well-organized, CRISP (a word I reserve for only the very best writing) – and weaves concepts, theory, and practical hands-on coding exercises – into a seamless narrative.
- The writing is fresh – and lively – filled with insights and examples that are meaningful to any reader interested in Causality Inference/Discovery/Modeling, Machine Learning, and Deep Learning.
- Near the end of the book, I came to a new appreciation for the care with which the author has taken to prepare and organize the information – in successive elegant layers – to enrich the reader’s learning experience. This is another confirmation of the exceptional quality of the writing – and the author’s depth of preparation to write this book.
Taken as a whole (the combination of the writer’s content – and the cornucopia of suggested follow-up references) – this book easily wins, hands-down, as the best book I’ve ever read, published by Packt.
From Amazon:
https://www.amazon.com/Causal-Inference-Discovery-Python-learning/dp/1804612987
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data
Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.
You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.
Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.
The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.
What you will learn:
- Master the fundamental concepts of causal inference
- Decipher the mysteries of structural causal models
- Unleash the power of the 4-step causal inference process in Python
- Explore advanced uplift modeling techniques
- Unlock the secrets of modern causal discovery using Python
- Use causal inference for social impact and community benefit
From Packt:
https://www.packtpub.com/product/causal-inference-and-discovery-in-python/9781804612989
"Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.
Table of Contents
1. Causality – Hey, We Have Machine Learning, So Why Even Bother?
2. Judea Pearl and the Ladder of Causation
3. Regression, Observations, and Interventions
4. Graphical Models
5. Forks, Chains, and Immoralities
6. Nodes, Edges, and Statistical (In)dependence
7. The Four-Step Process of Causal Inference
8. Causal Models – Assumptions and Challenges
9. Causal Inference and Machine Learning – from Matching to Meta-Learners
10. Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
11. Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
12. Can I Have a Causal Graph, Please?
13. Causal Discovery and Machine Learning – from Assumptions to Applications
14. Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond
15. Epilogue
No comments:
Post a Comment