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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python

Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

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Profile Icon Aleksander Molak
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (49 Ratings)
Paperback May 2023 456 pages 1st Edition
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$27.99 $31.99
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Arrow left icon
Profile Icon Aleksander Molak
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (49 Ratings)
Paperback May 2023 456 pages 1st Edition
eBook
$27.99 $31.99
Paperback
$39.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$27.99 $31.99
Paperback
$39.99
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Renews at $19.99p/m

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Causal Inference and Discovery in Python

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Key benefits

  • Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more
  • Discover modern causal inference techniques for average and heterogenous treatment effect estimation
  • Explore and leverage traditional and modern causal discovery methods

Description

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. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.

Who is this book for?

This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

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

Product Details

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Publication date : May 31, 2023
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612989
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Product Details

Publication date : May 31, 2023
Length: 456 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612989
Category :
Languages :
Concepts :
Tools :

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Table of Contents

20 Chapters
Part 1: Causality – an Introduction Chevron down icon Chevron up icon
Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? Chevron down icon Chevron up icon
Chapter 2: Judea Pearl and the Ladder of Causation Chevron down icon Chevron up icon
Chapter 3: Regression, Observations, and Interventions Chevron down icon Chevron up icon
Chapter 4: Graphical Models Chevron down icon Chevron up icon
Chapter 5: Forks, Chains, and Immoralities Chevron down icon Chevron up icon
Part 2: Causal Inference Chevron down icon Chevron up icon
Chapter 6: Nodes, Edges, and Statistical (In)dependence Chevron down icon Chevron up icon
Chapter 7: The Four-Step Process of Causal Inference Chevron down icon Chevron up icon
Chapter 8: Causal Models – Assumptions and Challenges Chevron down icon Chevron up icon
Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners Chevron down icon Chevron up icon
Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More Chevron down icon Chevron up icon
Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond Chevron down icon Chevron up icon
Part 3: Causal Discovery Chevron down icon Chevron up icon
Chapter 12: Can I Have a Causal Graph, Please? Chevron down icon Chevron up icon
Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications Chevron down icon Chevron up icon
Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond Chevron down icon Chevron up icon
Chapter 15: Epilogue Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(49 Ratings)
5 star 79.6%
4 star 8.2%
3 star 4.1%
2 star 0%
1 star 8.2%
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valdez ladd Nov 27, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book that show theory with practical examples and generous online references in the book.that avoids overloading the size and mission of the teachings
Feefo Verified review Feefo
N/A Feb 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book! With many practical examples that can be used as an initial guide to apply causal inference to real problems. The best causal inference book I've ever read (even better than "Causal Inference: for the Brave and True"!)
Feefo Verified review Feefo
Sangita Mahala Jul 31, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I highly recommend Causal Inference and Discovery in Python to anyone who wants to learn about causal inference and machine learning in Python. The book is well-written, informative, and easy to follow.
Amazon Verified review Amazon
Rohan Singh Rajput Dec 25, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As someone deeply passionate about the intersection of data science and causal inference, I recently finished reading "Causal Inference and Discovery in Python" by Aleksander Molak. It's a rare gem that brilliantly bridges theoretical concepts and practical application, and I can't help but share my thoughts with you all!🌟 Structure & Content:The book is ingeniously divided into three parts. The first section lays a solid foundation in causality, making a compelling case for its importance beyond conventional statistical methods. The chapters on Judea Pearl's Ladder of Causation are particularly enlightening.The heart of the book lies in Part 2, where Molak delves into the nuts and bolts of causal inference, integrating Python tools like DoWhy and EconML. It's a goldmine for practitioners, offering clarity and depth on complex topics.Part 3 is a forward-looking exploration into causal discovery, blending advanced topics like deep learning with practical applications. It's a testament to the book's commitment to staying at the forefront of causal machine learning.👨‍💻 Practicality & Application:What sets this book apart is its seamless blend of theory and practice. The Python code snippets and real-world examples are not just add-ons but core components that make complex ideas tangible and applicable.📖 Writing Style:Molak's writing is clear, engaging, and accessible. He tackles sophisticated concepts with an ease that speaks of his deep understanding and passion for the subject.🌍 Implications for Data Science:As data science continues to evolve, the importance of causal inference only grows. This book is more than just a resource; it's a guide that empowers you to implement causal inference in real-world scenarios, enhancing your analytical toolkit.In conclusion, "Causal Inference and Discovery in Python" is a must-read for anyone in data science, from students to seasoned professionals. Whether you're looking to deepen your understanding of causal inference or apply it in Python, this book is an invaluable asset.#DataScience #CausalInference #Python #MachineLearning #BookReview #AnalyticsCommunity👉 Highly recommend grabbing a copy if you're looking to stay ahead in the ever-evolving field of data science! 🌐📊🐍
Amazon Verified review Amazon
Judith Hurwitz Sep 11, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was eagerly anticipating this book because the author is so knowledgable about the technology behind causal AI and causal Inference. I was not disappointed. The book is very well written and provides the right level of business and technical understanding of an important but complex area. I strongly recommend this important book.
Amazon Verified review Amazon
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