This Post Has 13 Comments

1. Great information from theory to python
   I love the way it explains the theory with puthon 3xamplea, then uses libraries like econml and fonally introduces advanced techniques like deep learning always with easy to understand python code. Recomended.

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2. Unlocking the Power of Causal Inference
   Causal Inference and Discovery in Python is a valuable addition to the library of Data Scientists and researchers who are interested in Causal Inference. This book offers a comprehensive and practical guide to causal inference and discovery methods. The book starts with a solid foundation by explaining the fundamentals of causal inference and how it differs from Machine Learning. It takes readers through the concepts of causality, counterfactuals, direct acyclic graphs and causal discovery, making these complex ideas clear and understandable to a wide audience, from beginners to seasoned data scientists. The practical examples, along with clear explanations and code snippets, make it easy for readers to follow along and apply what they’ve learned.
   What sets this book apart is its strong emphasis on hands-on implementation. The author provides numerous real-world examples and practical exercises using Python libraries such as EconML, doWhy, gCastle. and Causica. These libraries enable readers to implement causal analysis techniques efficiently, which is essential for anyone looking to apply causal inference in their data projects.
   Another notable feature of the book is its attention to potential pitfalls and challenges in causal analysis. It doesn’t just stop at teaching the “how” but also delves into the “why” behind certain methodologies and the limitations of causal inference techniques. This level of depth and transparency is essential for building a deep understanding of the subject matter. The book also covers advanced topics like causal discovery algorithms, providing readers with a well-rounded overview for this particular area. While this book is a valuable resource for anyone interested in causal inference, it may not be suitable for absolute beginners in Python. Some prior familiarity with Python programming and basic data science concepts is recommended to fully grasp the content.
   In summary, Causal Inference and Discovery in Python is a commendable resource for those looking to demystify the complexities of causal analysis and apply it to real-world problems. Its hands-on approach, coupled with clear explanations and practical examples, makes it a must-read for data scientists, researchers, and analysts seeking to understand and leverage causal inference in Python.

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3. Contenido de calidad
   Excelente libro; entrega en tiempo indicado. Bien!

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4. I was skeptical, but I was wrong
   I bought this book because a friend recommended it to me.
   According to the “badge” on the cover, the book approaches causality from a “Pearlian and Machine Learning Perspective”.
   I was a bit skeptical at first, because I know Pearl’s work and it was hard for me to imagine someone could bring much new insight here.
   In hindsight, I must say this book is a great read. It provides the reader with very intuitive explanations and makes the transition from theory (sometimes pretty complex) to practice seamless.
   The book is very well written. The author’s attitude is positive but realistic. This makes reading the book not only a great educational experience, but also an intellectual adventure of sorts.
   Highly recommended, and thanks to Glenn for bringing this book to my attention!

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5. Causality using Puthon
   The book arrived earlier than indicated. That was good. The book is written for someone that has a basic understanding of causality without going into the mathematical aspects of causality. The book needs more information on getting started with Python for non- python users.

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6. Brilliant!
   The explanations are clear, beautiful, and well-researched. I’ve enjoyed it and learned a lot, and this book is a milestone in the current knowledge gap in contemporaneous causal inference.
   Packt publications are known for their lousy quality; this book is an exception and reflects the author’s genius. He is an able auto-editor with deep knowledge of the field of causal inference. For anyone reading this review, do yourself a favor and buy this book.

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7. formulas on kindle are not rendering properly
   On kindle for mac formulas are not rendering correctly i.e. no subscripts. Can you please fix it?

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8. A Must-Read for Data Aficionados and Business Pros Alike
   If you’re a data science enthusiast or a business professional keen on understanding the real impact of specific efforts, “Causal Inference and Discovery in Python” is your go-to guide. This book is a treasure trove of easily digestible information, supplemented by a wealth of resources to clarify complex concepts. It masterfully consolidates a series of whitepapers into one comprehensive resource. It is highly recommended for anyone looking to elevate their understanding of causal inference.

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9. Highly recommended for beginners and those who want to start empirical analysis using DoWhy

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10. Formulas on Kindle are not shown, a question mark is shown instead or very poorly shown

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11. I’m currently about 2/3rds of the way through this book and I’ve found it to be an excellent read. The author makes the complex simple with his well referenced and researched writing. Absolutely recommended for anyone interested in getting up to speed. It covers the basics as well as the most recent techniques like Double Machine learning, causal forests and more. Interested in causal analysis, then don’t hesitate. But it!

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12. I finished reading your book cover to cover! I really enjoyed it! It gives a fresh overview of the field and the active Python ecosystem! Amazing work! No surprise is a best seller 😉 100% Recommended!

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13. Was a present.

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