- Hardcover: 432 pages
- Publisher: Allen Lane (15 May 2018)
- Language: English
- ISBN-10: 9780241242636
- ISBN-13: 978-0241242636
- ASIN: 0241242630
- Product Dimensions: 13 x 3 x 21 cm
- Average Customer Review: 2 customer reviews
- Amazon Bestsellers Rank: #26,328 in Books (See Top 100 in Books)
The Book of Why: The New Science of Cause and Effect Hardcover – Import, 15 May 2018
|Hardcover, Import, 15 May 2018||
Customers who bought this item also bought
Customers who viewed this item also viewed
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 Prize author of Thinking, Fast and Slow)
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 author of The Master Algorithm)
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)
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.)
Modern applications of AI, such as robotics, self-driving cars, speech recognition, and machine translation deal with uncertainty. Pearl has been instrumental in supplying the rationale and much valuable technology that allow these applications to flourish (Alfred Spector, Vice President of Research, Google, Inc.)
About the Author
Judea Pearl (Author)
Judea Pearl is a world-renowned Israeli-American computer scientist and philosopher, known for his world-leading work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. In 2011, he won the most prestigious award in computer science, the Alan Turing Award. He has also received the Rumelhart Prize (Cognitive Science Society), the Benjamin Franklin Medal (Franklin Institute) and the Lakatos Award (London School of Economics), and he is the founder and president of the Daniel Pearl Foundation.
Dana Mackenzie, a Ph.D. mathematician turned science writer, has written for such magazines as Science, New Scientist, and Discover.
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter mobile phone number.
There was a problem filtering reviews right now. Please try again later.
The Book of Why is STUFFED with countless cases & concepts, dealing with Cause & Effect - aka the Science of Casualty.
Warning: This IS A NERDY, formulae laden book. If you can ignore the math & wade beyond, you will learn some things! E.g.
- How does your cell phone manage to convert your call and relay it to the other person almost perfectly? (Bayesian Networks)
- Path diagrams
- Belief propagation
- Monty Hall Paradox (amazing)
- How to train AI
"One of the crowning achievements of the Causal Revolution has been to explain how to predict the effects of an intervention without actually enacting it"
"Ancient Greek philosopher Democritus said, "I would rather discover one cause than be the King of Persia."
- "Galton conjectured that regression to the mean was a physical progress...nature's way of ensuring and adjusting distribution..." (my fav)!
Take a shot at the book. It's complicated BUT worth it
Most helpful customer reviews on Amazon.com
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.