作者:
Judea Pearl
/
Dana Mackenzie 出版社: Basic Books 副标题: The New Science of Cause and Effect 出版年: 2018-5-15 页数: 432 定价: USD 32.00 装帧: Hardcover ISBN: 9780465097609
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
· · · · · ·
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.
不好意思，实在看不下去。。相比之下我还是选择Causal Inference by Miguel Hernán and Jamie Robins， 或者Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction by Guido Imbens和Don Rubin
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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xusparrow
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咸鱼吴
2 有用 H 2019-09-21 12:16:16
三星半。书名应该是The book of why data isn't everything. 因果关系定义或者说哲学基础的缺乏使得所有讨论显得只是在反驳数据的推崇者：没有模型的统计不可能推导出因果关系。一些技术的部分受限于科普的题材又讲得不够清楚，不如直接读causality。
0 有用 童彤 2020-05-20 03:20:29
不好意思，实在看不下去。。相比之下我还是选择Causal Inference by Miguel Hernán and Jamie Robins， 或者Causal Inference for Statistics, Social, and Biomedical Sciences : An Introduction by Guido Imbens和Don Rubin
0 有用 arale 2019-08-03 18:09:35
详细解读了相关性和因果性的本质区别，提出了基于数学推导，结合symobolic的人类知识和numerical的数据的解决方法
0 有用 青铜 2018-09-30 14:34:11
学习一哈
1 有用 EM 2018-09-09 07:25:06
老爷爷的书刷第二遍了，怀着无比崇敬的心情。我想我的新年愿望就是把这书再刷几遍，然后争取看懂老爷爷的其他书
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。
0 有用 A100101 2023-02-28 08:43:33 加拿大
CS\Causal Inference 科普
0 有用 masterplan 2023-01-23 19:31:22 新加坡
半部统计史 比较啰嗦 其实后来强化学习走的就是因果的路
0 有用 billism 2022-11-05 22:48:52 四川
在 iPad 上放了好几年才读完, 作者的写作风格比较一言难尽 [允悲]
0 有用 豆友183248999 2022-05-07 14:23:43
370/1710 = 1340