具体描述
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.
目录信息
读后感
这本书说的是人类思维中最重要的逻辑关系——因果关系。 人类的大脑中有强烈的因果直觉，这种直觉在正向判断中非常高效。当看到一件事情时，我们能够很有把握地判断出它可能导致的结果。但是反过来，我们的直觉往往不够有效。也就是说，当看到结果时，我们常常无法快速准确地推...
评分 评分这本书说的是人类思维中最重要的逻辑关系——因果关系。 人类的大脑中有强烈的因果直觉，这种直觉在正向判断中非常高效。当看到一件事情时，我们能够很有把握地判断出它可能导致的结果。但是反过来，我们的直觉往往不够有效。也就是说，当看到结果时，我们常常无法快速准确地推...
评分用户评价
Not my book though
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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
评分去年nips有眼不识泰山没去听老爷子的talk，作为初级炼丹工看这本面向大众的新书补课也很开眼界。“相关不蕴涵因果”讲得多了都不知道所谓因果关系究竟是什么。仅靠拟合数据，不管是用深度学习还是多fancy的方法，都无法表示因果关系；要谈论因果乃至虚拟事实，须明确引入数据以外的假设，而书中也指明了什么样的假设配上什么样的数据可以回答什么样的因果问题。现实生活中很多问题都不能做随机对照试验，这套理论也因此格外重要。要是老爷子再谈谈他对强化学习的看法就好了。
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