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 Pe...
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.
作者简介
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Judea Pearl is a professor of computer science at UCLA and winner of the 2011 Turing Award and the author of three classic technical books on causality. He lives in Los Angeles, California.
Dana Mackenzie is an award-winning science writer and the author of The Big Splat, or How Our Moon Came to Be. He lives in Santa Cruz, California.
rather than a new science. 1，作者并没有区分自然科学和社会以及行为科学，没有讨论这两个领域因果推断的异同，也没有上升到科学哲学的层面讨论因果推断本身。这些本身都不是问题。只是就内容来说，书中的science实际上指的是社会科学和行为科学，作者所说的“因果革命 (the ...
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一周把Book of Why（BOW）和他那本Causal Inference in Statistics: A Primer（Primer）看完了。非常精彩。 从时间顺序上而言，其实是Primer先写完，BOW才开始动笔的。但是两本书参照着看就能明白，BOW里所有的技术框架，都是依照着Primer来写的。BOW是在这个骨架上讲了一个Cau...
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2 有用 陈德柱 2018-10-09 09:17:29
科普，好看，推荐。（时隔好多年又重新学习学校里讲过的东西我的心情可以用沮丧来形容）（第一次读评分10的书，激动）（背景音pulp）
0 有用 独孤力命 2018-08-21 22:23:30
总算有本Judea Pearl的书是我能看懂的了，虽然是科普……读下来的感觉，Pearl的工作将人类直觉化的因果推理能力用数学形式表达了出来，使causal effect成为可以估计的变量。但因果模型如何提出，如何验证，似乎并没有涉及太多。如果强人工智能需要学会因果推理，提出模型应该比估算模型要难得多，也重要得多。
0 有用 小丫么小虾米 2023-06-28 05:33:34 美国
就像小孩子总喜欢问“为什么”，一种动物性的本能，生出了动物性的直觉。除此之外，基于大量的统计思维和逻辑，倒是没多少新意。
4 有用 里托·贝森 2018-09-07 23:59:53
每个人都是一部因果关系自动机。真要把人脑对因果的思维过程掰扯明白，还真是不容易。作者的因果模型，是把复杂问题简单化的经典例子了。
0 有用 羊 2019-10-01 11:41:58
还没读完，但是绝对的好书一本，尤其是在分析领域工作的人，打破“相关非因果”的分析瓶颈。