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1. A Summary of a Lifetime of Scientific Work with Implications for all of Humanity
   The Book of Why is a popular introduction to Judea Pearl’s branch of causal inference. But it is also so much more.Pearl has written many other textbooks introducing his graphical approach. But in this book, Pearl provides an engaging narrative of the history of causal inference, the important distinctions he sees in his branch and its importance for the future of Artificial Intelligence.Briefly, Pearl views classical statistics as seriously flawed in not having developed a meaningful theory of causality. While able to demonstrate correlation, Pearl asserts that in classical statistics all relationships are two-way: that is 2x=3y+6 can also be written 3y=2x-6. We are left in doubt as to whether x causes y or y causes x.Fundamentally, Pearl sees this problem as still plaguing all artificial intelligence and statistics. In its place, Pearl argues that the exact causal relationship between all variables should be explicitly symbolized in graphical form and only then can mathematical operations tease out the precise causal effect.To be transparent, I am trained in the Rubin approach to causal inference and disagree with some of Pearl’s history and characterization of statistics. But that is not the point. The history is well-written, engaging and understandable by the lay reader. Similarly, his account of graphical causal inference theory is followable even for someone like myself who did not learn these techniques in graduate school.The last part of the book, where Pearl opines on the future of AI, is the most sensational. Pearl believes that if computers were programmed to understand his symbolization of causal inference theory they would be empowered to realize counterfactuals and thus engage in moral decision making. Furthermore, since Pearl himself was a pioneer in deep learning, his characterization of contemporary AI as hopelessly doomed in the quest to replicate human cognition because of a lack of understanding in causal inference will be sure to garner attention.But one would be misguided to think that speculations about AI or mischaracterizations of other kinds of causal inference make this book any less of a classic. For the first time, Pearl has written a popular, interesting and provocative book describing his branch of causal inference theory—past, present and future.This book is a must read then, not only for causal inference theorists, but more widely for those with any interest in contemporary developments in computer science, statistics or Artificial Intelligence. A book that, like Kahneman’s Thinking Fast and Slow, is a triumphant summary of a lifetime of work in scientific topics that have ramifications, not only for fellow scientists, but for all of humanity.

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2. Very interesting read
   Someone recommended this book to me based on my interest in causality, specifically Goldratt’s thinking processes and his change question sequence (why change, what to change, what to change to, how to cause the change and how to measure and sustain the change). I had long thought AI was snake oil, with its use in areas where it shouldn’t be used. Pearle describes the problem with AI in the title, The Book of Why. AI tries to predict upward from correlations. One must down determining why a problem exists and the assumptions around that cause, not predict blindly from a lack of understanding of the underlying system’s interrelationships. I attended an academic conference, and as you might imagine, the hot topics were AI and data analytics. There was no research question, no propositions and conclusions, no working hypotheses, just very large data sets and the application of numerous statistical models to determine what might be correlated. DATA DREDGE! Much of Pearle’s book was above my head, but I suggest that he study Goldratt’s thinking processes and his categories of legitimate reservation (rules of logic) and teach readers to build a system model of the environment before applying AI. I believe AI is a powerful new methodology, BUT I fear we will have decades of misapplication and wasted brainpower studying the wrong problems where simple logic would be a better alternative when applied using the question of WHY to dive deeper and deeper into our understanding of causalities before blindly applying AI. Great read for me. I applaud any author who wants to ask WHY like a scientist does. Please check out TOC for Education, where kids learn simple logic tools to improve their lives.

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3. Introduces powerful ideas about cause and effect
   Well-written, fairly complex book. Introduces important concepts but not easy going. Definitely recommended as it contains important and challenging ideas with great historical background. Learned a lot but clearly this book requires your full attention.

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4. Good science in accessible language.

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5. Livro ótimo pra entender sobre correlação e causalidade

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6. Detallado en la exposición del tema.

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7. I read and comprehend until Chapter 4. The book starts to be difficult. The certain background is needed to understand. Not for everyone. The book is even not easy for epidemiologists and staticians. The scientists even don’t know the Causal Theory.

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8. accessible written style with simple and meaningful examples.fresh air in the world of “big data is all we need”.core idea : any scientist needs a model of the real world he wants to represent + you can not escape the problem of causes and effects, even with massive data bases and statistics.

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