The Book of Why pdf epub mobi txt 电子书 下载 2025
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.
- 统计
- 逻辑
- 方法论
- 计算机
- 因果
- 哲学
- 科普
- AI
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.
具体描述
读后感
1.作者朱迪亚·珀尔，是加州大学落砂机分校的计算机科学教授，计算机最高奖项图灵奖的获得者，被称为贝叶斯网络之父。作为科学家写的科学著作，这本书需要一定的先验知识才能阅读下去，起码读者应该对概率论有基本的了解。不过，虽然有一定的阅读难度，但能把人类关于因果关系...
评分 评分豆瓣要求1周出书评确实有些强人所难，以本书的内容含量来看，是值得开一年的读书会来反复研读的“新经典”。我们或许目睹了《自然哲学的科学原理》、《物种起源》相同级别的书诞生，何其幸哉。如果用一句话来为本书作品，那就是：这是一本你不看也值得买来摆在书架上的书。 本...
评分作为一名学习经管类专业的学生，这本书给了我许多更深入的思考。作者作为人工智能领域的专家，对于因果关系的理解鞭辟入里，使人茅塞顿开。例如开篇提及，在统计学课程上，学生们经常被教导“相关性不代表因果”，但往往很多的教导都止步于此——学生们知道了什么不是因果，却...
用户评价
学统计教统计十几年，好多核心的概念第一次看人讲得这么清楚，豁然开朗豁然开朗！
评分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
评分从公司图书馆借得此书，翻了前两章，结合得到上万维钢的讲解，大致了解了因果关系的重要性和对下一步强AI的启发，为什么要超越相关性去探求因果性。如作者在前言末尾讲到的：“Data do not understand cause and effects; human do. I hope that the new science of casual inference will enable us to better understand how we do it, because there is no better way to understand ourselves than by emulating ourselves. ”
评分知其所以然。
评分从公司图书馆借得此书，翻了前两章，结合得到上万维钢的讲解，大致了解了因果关系的重要性和对下一步强AI的启发，为什么要超越相关性去探求因果性。如作者在前言末尾讲到的：“Data do not understand cause and effects; human do. I hope that the new science of casual inference will enable us to better understand how we do it, because there is no better way to understand ourselves than by emulating ourselves. ”
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