--- product_id: 147338917 title: "Book of Why" price: "638 kr" currency: NOK in_stock: true reviews_count: 13 url: https://www.desertcart.no/products/147338917-book-of-why store_origin: NO region: Norway --- # Book of Why **Price:** 638 kr **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Book of Why - **How much does it cost?** 638 kr with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.no](https://www.desertcart.no/products/147338917-book-of-why) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description An argument for how understanding causality has revolutionized science and will revolutionize artificial intelligence. "Illuminating." ― New York Times "Extraordinary." ― Science Friday “Correlation is not causation.” This mantra, chanted by scientists for more than a century, once led to a virtual prohibition on causal talk. Today, with causal analysis taking center stage in AI and other applications, the old taboos are a thing of the past. The causal revolution, instigated by Judea Pearl and others, established causality on a firm scientific basis. Pearl showed how intuitive models can interface with data to help answer causal questions, such as whether a drug will cure an illness or, counterfactually, whether an illness would have persisted absent the drug. These models provide a blueprint for causal AI to learn and take responsibility for its mistakes. Updated with a new preface and summary of recent advances, The Book of Why outlines the basics of cause and effect for lay readers and scientists alike, providing tools to explore the observable world and worlds that might have been. Review: 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. Review: A fascinating introduction to causal reasoning - 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. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #51,588 in Books ( See Top 100 in Books ) #1 in Biostatistics (Books) #4 in Discrete Mathematics (Books) | | Customer Reviews | 4.4 out of 5 stars 2,465 Reviews | ## Images ![Book of Why - Image 1](https://m.media-amazon.com/images/I/71rhX-7PJmL.jpg) ## Available Options This product comes in different **Media Language, Media Format** options. ## Customer Reviews ### ⭐⭐⭐⭐⭐ A Summary of a Lifetime of Scientific Work with Implications for all of Humanity *by A***S on May 17, 2018* 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. ### ⭐⭐⭐⭐⭐ A fascinating introduction to causal reasoning *by D***L on June 27, 2019* 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. ### ⭐⭐⭐⭐ great for what it is. *by M***4 on September 24, 2021* Given a valid causal framework, this book shows how to use collected data to answer previously unanswerable questions. I’m convinced the process is good. It doesn’t dive into causal discovery or the process of validating a causal framework—that is left to the scientist/user. This is the missing link, and it’s a huge gap because without this link his process is worthless. It is up to you still to make his process work. The largely undetected/unacknowledged limitation on AI/ML is their inability to validate the generalizations they make during training and use during testing/fielding because they invoke simple enumeration to find associative relationships or correlations and not causal relationships. The author mentions there is no science without generalizing (without induction), but does not cover how to validate generalizations, which is desperately needed if AI is to act on valid generalizations. The scientific method properly understood is a method of induction to find causal relationships via method of difference and similarity. The author’s understanding of the scientific method falls short and adopts the common understanding found in most textbooks, which is wrong and hinders causal discovery. His said he wouldn’t define casualty, and provides these reasons in chapter 1: “Any attempt to ‘define’ causation in terms of seemingly simpler, first-rung concepts must fail. That is why I have not attempted to define causation anywhere in this book: definitions demand reduction, and reduction demands going to a lower rung.” But he defined it only a chapter before in the introduction and says it’s simple: “the definition of “causation” is simple, if a little metaphorical: a variable X is a cause of Y if Y “listens” to X and determines its value in response to what it hears.” This is a huge editorial oversight and is likely to confuse the reader. The definition he gives is good enough to understand what he’s talking about, and good enough to generally reveal the value of his method, but it’s inadequate for going the next step of causal discovery. The way Aristotle considered causality is the application of the law of identity applied to action—this is the proper conceptualization of causality in my view. Given on object with X properties, doing Y to the object will cause the object to do Z every time because of its X properties. This allows us to generalize because everything with X properties will necessarily have to act the same way, Z, when doing Y to it. Y causes object X to do Z, because it is X; logically translates to all X will do Z when Y acts on it—the generalization. The scientific method when properly understood and applied is a method to discover “Y causes object X to do Z, because it is X” thus allowing the validated generalization “all X will do Z when Y acts on it”. This in short is the missing link we all need if the generalizations we use and act on are to be valid. ## Frequently Bought Together - Book of Why - Causal Inference (The MIT Press Essential Knowledge series) - Causality: Models, Reasoning and Inference --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.no/products/147338917-book-of-why](https://www.desertcart.no/products/147338917-book-of-why) --- *Product available on Desertcart Norway* *Store origin: NO* *Last updated: 2026-09-17*