Book of Why

by Pearl, Judea
Book of Why cover
Good Books rating 4.5
Technical
  • ID: 1894
  • Added: 2025-10-11
  • Updated: 2025-10-11
  • Reviews: 2
Reviews
bayes.cs.ucla.edu · Unknown · 2025-10-15
insightful 4.50

The Book of Why offers a compelling exploration of causality, making complex concepts accessible. It highlights the importance of understanding cause and effect in various fields, though some may find the technical aspects challenging.

The Book of Why by Judea Pearl and Dana Mackenzie delves into the fascinating world of causality, presenting it in a way that is both engaging and accessible. The authors successfully demystify complex ideas, making them understandable for a broad audience. They emphasize the significance of causality in fields ranging from science to everyday life, providing practical examples and insights. However, the book does contain some technical sections that might be challenging for readers without a background in statistics or mathematics. Overall, it is a valuable read for anyone interested in understanding the underlying principles of cause and effect. The book's strength lies in its ability to bridge the gap between theoretical concepts and real-world applications, making it a worthwhile addition to any bookshelf.

Quick quotes

    The book makes a compelling case for the importance of causality in understanding the world around us.

    Pearl and Mackenzie do an excellent job of breaking down complex ideas into digestible pieces.

    The technical sections, while informative, may be challenging for some readers.

aiws.net · Unknown · 2025-10-15
convincing 4.50

The Book of Why by Judea Pearl and Dana Mackenzie is a timely and important book that challenges the limitations of data-driven approaches to AI and emphasizes the need for causal models. The authors argue that true AI requires understanding causation, not just statistical associations, and present Pearl's 'Ladder of Causation' to illustrate this point.

The Book of Why by Judea Pearl and Dana Mackenzie is a significant contribution to the field of AI and causality. The book is timely and important, as it challenges the dominant idea that big data and machine learning alone can provide real intelligence. The authors argue that true AI requires understanding causation, not just statistical associations, and present Pearl's 'Ladder of Causation' to illustrate this point. The book is written for a general audience and is accessible, making it a valuable resource for anyone interested in AI and causality. The authors also provide a convincing demonstration of the need for causal models in areas such as medicine, criminology, marketing, finance, and public policy. The book is a must-read for anyone interested in the future of AI and the role of causality in decision-making.

Quick quotes

    The Turing award is the highest distinction in computer science; i.e., the Nobel Prize of computing.

    We have long been sceptical of the dominant idea that when 'big data' is coupled with sophisticated machine learning algorithms they can provide ‘real’ intelligence.

    All the impressive achievements of deep learning amount to just curve fitting.

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