--- product_id: 147338917 title: "Book of Why" price: "9.87 OMR" currency: OMR in_stock: true reviews_count: 13 url: https://www.desertcart.com.om/products/147338917-book-of-why store_origin: OM region: Oman --- # Book of Why **Price:** 9.87 OMR **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Book of Why - **How much does it cost?** 9.87 OMR with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.com.om](https://www.desertcart.com.om/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 The "extraordinary" (Science Friday), "illuminating" ( New York Times ) argument for 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 . 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 | #15,100 in Books ( See Top 100 in Books ) #6 in Probability & Statistics (Books) #36 in History & Philosophy of Science (Books) #49 in Artificial Intelligence & Semantics | | Customer Reviews | 4.4 out of 5 stars 2,431 Reviews | ## Images ![Book of Why - Image 1](https://m.media-amazon.com/images/I/71rhX-7PJmL.jpg) ## 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. ### ⭐⭐⭐⭐ Why? Your child knows. Your robot does not. (And data alone won't solve the problem.) *by G***R on February 1, 2022* It is doubtful that Professor Pearl is at all surprised by the polarity in the reviews of this book. I imagine, in fact, he has a slight smile on his face. This is a man that clearly does not cower from a debate. To me this is not so much a book about science but a book about statistics, which are used almost universally. Many of his examples involve science – hard or social is irrelevant – because that is the world he knows. My world is business, and I can tell you from experience that everything he says about the disregard for causality and the limitations of linear statistics using data alone is spot on. The book covers many fronts but the overarching theme is causality. Why? When we investigate cause and effect how do we know that we have reached the right conclusion without challenging that conclusion, both intuitively and using the tools of mathematics? One of the great myths of science today is that we have conquered the causality problem. We haven’t. Most scientific discoveries are ultimately proven wrong, or at least incomplete. Major drug studies cannot be replicated. And peer review alone – the gold standard of proper science – is not, by itself, any guarantee of truth. In a recent study of scientific papers published on COVID-19, all of which were peer reviewed before being published in prestigious journals, the researchers found that a surprising number ultimately had to be retracted. In my world, the world of business, the results are both staggering and a bit horrifying. A large percentage of students graduating from university today with an interest in business have degrees in something to do with data: data mining, data analysis, Big Data. Data is the new marketing. If you want to launch a program or make an investment you must first make the “business case.” That means you must create a statistical case, almost always based on data. Unfortunately, these cases are often wrong and businesses continue to make bad investments. The preoccupation with data is based on the belief that “data are facts.” But that’s only partially true. Data are facts only in a specific context. And there can be an infinite number of contexts in the real world, a world that is constantly changing. With data in hand, people are no longer asking why. They are no longer even bothering to access their intuition to ask what they might be missing. Intuition, in fact, has become a dirty word, something akin to voodoo or folklore. When it comes to AI, Professor Pearl notes that we are not as far along as many people assume. We are decades away from AI that is even remotely humanlike. Because, as Pearl notes, machines cannot imagine what isn’t. They cannot ask why at even the simplest level. Yet humans, even young children, do it all the time. At least we used to. Which is why I don’t believe we will ever create AI that is humanlike. We don’t yet understand how or why humans think intuitively and what prompts us, or allows us, to imagine alternate realities. How can we teach machines to do it? We can only use algorithms, piled one on top of the other, to calculate a probable answer. And while machine learning can make these algorithmic machines incrementally more accurate, I do not wish to defer to an incrementally more accurate answer when it comes to the big issues of life and society. Or my health. It has been widely reported, for example, that the engineers of Google are no longer entirely sure how their search engine works. It’s too complex. Which is why modifications are not just calculated and applied. They are tested first, on a large test database, to see what results they get. Those results are then reviewed intuitively to see both if they make sense and are what the engineers expected. And that is how we should treat all statistics. Why? Why? Why? Professor Pearl has given us some tools to help in the process. But he has not given us a final solution, as even he admits. Nonetheless, he has moved us down the path. His methods still require assumptions and work largely in the world of probabilities. This book will be a tough read if you are uncomfortable with mathematics. And there are a lot of models and formulas that will be impossible to decipher if you don’t speak the language of mathematics. In every case where he offers a formula, however, he explains what it says, so that while he admits a personal fondness for formulas you can really just ignore them and still get a lot from this book. He is a little harsh, however, regarding other people in the scientific world, past and present, some of whom have obviously offended him in the past. I found that a little off-putting, which is the only reason I didn’t rate the book a 5. Nonetheless, this is an insightful book by a passionate man and I believe I invested my time wisely in reading it. ## 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.com.om/products/147338917-book-of-why](https://www.desertcart.com.om/products/147338917-book-of-why) --- *Product available on Desertcart Oman* *Store origin: OM* *Last updated: 2026-07-15*