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

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.

Casual Inference and Discovery in Python is a great book - it's approachable, clear, filled with examples and code to follow through.I'm only in the first half of the book, but I feel that it's helping me with my perspective as a data scientict.Causation is not correlation, so this book won't make you better, but there's a good correlation between reading it and feeling like you have more tools to solve real-world problems

This book is helping me understand the fundamental concepts of causal inference and the various application methods. My journey started with standard statistics, then to bayes, and now causal models. The practical, hands-on book exercises clarified and cemented the many new (to me) concepts unique to causal modeling. I appreciate Mr. Molak taking time to write this excellent book.

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.

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!

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.

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

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.