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  • The Book of Why: The New Science of Cause and Effect

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The Book of Why: The New Science of Cause and Effect Hardcover – May 15, 2018

4.4 out of 5 stars (2,409)

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A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs
The Book of Why.

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From the Publisher

The "extraordinary" (Science Friday), "illuminating" (New York Times) argument for how understanding

Blurb from New York Times for THE BOOK OF WHY

Blurb from Jewish Journal for THE BOOK OF WHY

Blurb from Science Friday for THE BOOK OF WHY

Editorial Reviews

Review

One of Science Friday's "Best Science Books of 2018"

"Illuminating... The Professor Pearl who emerges from the pages of
The Book of Why brims with the joy of discovery and pride in his students and colleagues... [it] not only delivers a valuable lesson on the history of ideas but provides the conceptual tools needed to judge just what big data can and cannot deliver."―New York Times

"Cause and effect is one of the most heavily debated, difficult-to-prove things in science and medicine. This book really gets you thinking about cause and effect as it applies to issues of our time, such as: How come cigarettes were around for years and we never showed they were causing cancer or heart disease? The authors goes through these cases like an interrogation, and it's just extraordinary."―
Science Friday

"Seriously, everyone should read
The Book of Why."―Jeff Witmer, American Mathematical Monthly

"'Correlation is not causation.' That scientific refrain has had social consequences...Judea Pearl proposes a radical mathematical solution...now bearing fruit in biology, medicine, social science and AI."―
Nature

"Lively and accessible...Pearl was one of the visionary leaders of the causal revolution, and
The Book of Why is his crowning achievement."―Jewish Journal

"Anyone interested in probing connections between cause and effect, and their relevance for the future of AI, will find this a fascinating and provocative book. Highly recommended."―
CHOICE

"Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. ... With the release of this historically grounded and thought-provoking book, Pearl leaps from the ivory tower into the real world...Pearl has given us an elegant, powerful, controversial theory of causality."―
American Mathematical Society

"Have you ever wondered about the puzzles of correlation and causation? This wonderful book has illuminating answers and it is fun to read."―
Daniel Kahneman, winner of the Nobel Memorial Prize in Economic Sciences and author of Thinking, Fast and Slow

"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence... and they have redefined the term 'thinking machine.'"―
Vint Cerf, Chief Internet Evangelist, Google, Inc.

"Judea Pearl has been the heart and soul of a revolution in artificial intelligence and in computer science more broadly."―
Eric Horvitz, Technical Fellow and Director, Microsoft Research Labs

"If causation is not correlation, then what is it? Thanks to Judea Pearl's epoch-making research, we now have a precise answer to this question. If you want to understand how the world works, this engrossing and delightful book is the place to start."―
Pedro Domingos, professor of computer science, University of Washington, and author of The Master Algorithm

"The Book of Why ... questions and redefines the building blocks of our AI systems"―theverge.com

About the Author

Judea Pearl is chancellor’s professor of computer science at UCLA. The author of three foundational books in AI, he has won numerous awards, including the Alan Turing Award. He lives in Los Angeles, California.  

Dana Mackenzie is a PhD mathematician turned science writer. He has written or co-written fourteen books, mostly on mathematical topics. He lives in Santa Cruz, California.

Product details

  • Publisher ‏ : ‎ Basic Books
  • Publication date ‏ : ‎ May 15, 2018
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 432 pages
  • ISBN-10 ‏ : ‎ 046509760X
  • ISBN-13 ‏ : ‎ 978-0465097609
  • Item Weight ‏ : ‎ 1.42 pounds
  • Dimensions ‏ : ‎ 6.3 x 1.4 x 9.4 inches
  • Best Sellers Rank: #528,554 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.4 out of 5 stars (2,409)

About the authors

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Customer reviews

4.4 out of 5 stars
2,409 global ratings

Customers say

Customers find the book insightful and an excellent introduction to causal thinking, with one review highlighting its engaging narrative of the history of causal inference. Moreover, the writing quality is well-received, and customers consider it useful, with one noting it's particularly relevant for software engineers. However, the book receives mixed feedback regarding its accessibility, with some finding it understandable while others consider it difficult to read. Additionally, opinions are divided on the subject matter, with some appreciating the interesting topic while others find it not scientific enough.
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81 customers mention content, 65 positive, 16 negative
Customers find the book insightful and useful, describing it as an excellent introduction to causal thinking.
I love probability. It is such a great book and everyone should read.Read more
Great read for anyone that likes statistics!Read more
Excellent book. Motivating very good accessible treatment of the "new science" of causal inference. Only weak point is chapter on the do-calculus....Read more
Heavy going but very interesting. I am not done yet because I keep needing to reread sections.Read more
16 customers mention subject matter, 14 positive, 2 negative
Customers find the book's subject matter interesting, with one customer noting how it helps make sense of data, while another appreciates how it addresses common research challenges.
Very well written book on an important topic. Not exactly for the lay person....Read more
...Definitely recommended as it contains important and challenging ideas with great historical background....Read more
This is an excellent book. It is well written and addresses important topics.Read more
...Generally accessible, but intellectually demanding. Introduces the ladder of causation as background for understanding the history of statistics....Read more
13 customers mention writing quality, 13 positive, 0 negative
Customers find the book well written, with one review specifically praising its engaging prose about the topic.
Very well written, and informative. Summarizes recent research that is relevant for most software engineers, statisticians and data scientists.Read more
This is an excellent book. It is well written and addresses important topics.Read more
Very well written book on an important topic. Not exactly for the lay person....Read more
...one of the greatest AI theorists of our time, but also an excellent writer, very good at explaining complex things in a simple manner....Read more
9 customers mention causality, 7 positive, 2 negative
Customers appreciate the book's treatment of causality, with one review highlighting its engaging narrative of the history of causal inference and another noting its historical narratives.
...as it contains important and challenging ideas with great historical background. Learned a lot but clearly this book requires your full attention.Read more
...talking about such an nuanced advance topic like Causal models and causality. I will be updating my review as I go further in the book.Read more
...The book covers why causation is crucial, how the very concept of causation became taboo, and the burgeoning causation revolution that is enriching...Read more
...There was no research question, no propositions and conclusions, no working hypotheses, just very large data sets and the application of numerous...Read more
9 customers mention informative, 9 positive, 0 negative
Customers find the book informative and useful, with one customer noting it is relevant for software engineers.
...are illuminating and useful.Read more
...Definitely recommended as it contains important and challenging ideas with great historical background....Read more
...Not exactly for the lay person. But very useful if you are a scientist but not an expert on statistics or causality.Read more
...Data is useful. But, data is not all. Causality hits at the heart of how we will eventually reach practical autonomous systems.Read more
15 customers mention non-technical, 8 positive, 7 negative
Customers have mixed opinions about the book's technical content, with some finding it not scientific at all, while others appreciate its accessible approach to statistics and causality.
Non-technical and yet dense, a lot of useful information, very easy to read, I loved it....Read more
...effect, and the simple answer is that the social "sciences" is not real science....Read more
...Its science is real, its problems intriguing, and its implications compelling....Read more
...He's not a statistician and, as the picture above shows, his statistical and data science abilities should be truly doubted....Read more
14 customers mention readability, 8 positive, 6 negative
Customers have mixed opinions about the book's readability, with some finding it understandable for lay readers while others describe it as a difficult read.
I had hoped for a fun, easy to read, informal book on causal inference. This isn't it....Read more
...Even with a magnifying glass, I am unable to read most of the content of the diagrams....Read more
...This all makes sense and looks elegant on paper. But to arrive at it is a different matter....Read more
...Introduces important concepts but not easy going....Read more
8 customers mention complexity, 2 positive, 6 negative
Customers find the book's complexity challenging, with one customer noting it is too mathematical, while another mentions the reasoning can be circular.
...anything or link any cause to effect, simply because economic systems are far too complex and can't be separated and isolated like a lab experiment....Read more
...The reasoning is often circular. Graphs meant to illustrate causality do so only when the premise assumes causality. ......Read more
Well-written, fairly complex book. Introduces important concepts but not easy going....Read more
...This is your book. The book avoids math and it is rather optimistic in terms of the actual application, but it is great....Read more
Another Key Contribution from Judea Pearl
5 out of 5 stars
Another Key Contribution from Judea Pearl
The contributions of Judea Pearl were quite important for me and my PhD students at Duke. His work was instrumental in our Bayesian network modeling of surface water quality for both prediction and causal assessment. This new book will give me insight as to how best to explain to my non-mathematical colleagues why our modeling has a causal basis and hence why it should be considered for insight into decision making. I commend Dr, Pearl for addressing this need for a book that can reach a larger audience.
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Top reviews from the United States

  • Reviewed in the United States on May 17, 2018
    Format: HardcoverVerified Purchase
    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.
    376 people found this helpful
    Report
  • Reviewed in the United States on June 27, 2019
    Format: HardcoverVerified Purchase
    The book's subtitle, The New Science of Cause and Effect, aroused both my skepticism and my curiosity: skepticism because I wondered how such a science could possibly be new, curiosity because I wanted to find out. The authors explain: Causal reasoning is ingrained in us and essential to our thinking, yet the human and social sciences often shy away from it, partly because they lack the proper models for its application. To stay on the safe side, people often speak in terms of "correlation" rather than causation. But this just evades the problem of causality, which can actually be described and tackled. The book shows how.

    Reading it slowly, I reached the point where I could understand the explanations of the diagrams and formulas. I especially enjoyed Chapters 6 and 8 (on paradoxes and counterfactuals, respectively). Yet I was well aware, along the way, that to truly understand this subject--that is, to be able to create and apply causal models on my own--I would need to read the book several times, work through each of the examples, and then work independently on related problems. Even then, I could not guarantee that I would do this well, since causal reasoning requires careful analysis of the problem at hand: of all the variables involved in it and their causal relationship to each other.

    Take, for example, the discussion of the smoking/cancer debate in chapter 5. Those who doubted that smoking causes cancer--R. A. Fisher and Jacob Yerushalmy among them--posited a constitutional factor, a so-called "smoking gene," that would predispose a person not only to smoking, but to other unhealthy behaviors that can likewise lead to cancer. Pearl and Mackenzie demonstrate, through causal diagrams, that such an explanation of the smoking-cancer relation is implausible. That is, even if such a gene exists (and it does), it does not erase the direct causal relationship between smoking and cancer. This all makes sense and looks elegant on paper. But to arrive at it is a different matter. The book does not turn anyone into an expert; rather, it helps readers at all levels perceive the scientific problems more clearly.

    I have many books waiting for me, but this is one that I hope to reread. Its science is real, its problems intriguing, and its implications compelling. With models for causal reasoning, we can tackle issues like global warming with greater clarity and confidence. We don't have to choose between unwarranted conclusions and flailing uncertainty. Causal reasoning allows us not only to pose clearer questions, but to work our way toward answers. The Book of Why opens up a promising field.
    12 people found this helpful
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  • CG
    5.0 out of 5 stars Buen libro
    Reviewed in Mexico on January 10, 2022
    Format: PaperbackVerified Purchase
    Detallado en la exposición del tema.
    Report
  • Marcos Augusto Burgos Saavedra
    5.0 out of 5 stars Loved the book
    Reviewed in Australia on July 16, 2025
    Loved the book
  • Jaguarella
    5.0 out of 5 stars Um pacote com matemática, probabilidade, estatística e inteligência artificial
    Reviewed in Brazil on October 28, 2025
    Format: PaperbackVerified Purchase
    Livro interessante e muito informativo para quem se interessa por matemática, inteligência artificial.
  • Victor Li
    5.0 out of 5 stars Easy to read
    Reviewed in Singapore on May 27, 2021
    Format: HardcoverVerified Purchase
    it is good to read no matter which area of study you are doing. I am doing research in CS and found this one quite useful. The book is written in a casual and easy understanding way.
  • Ivan Sarmiento
    5.0 out of 5 stars Good science
    Reviewed in Canada on January 15, 2025
    Format: PaperbackVerified Purchase
    Good science in accessible language.