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.
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.
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 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Heckman, Rubin, Pearl的爱恨情仇啊。From Gelman, Pearl’s obnoxiousness obstructs the disemmination of his ideas. And works by economists are swept under the rug. 画图容易,但用Rubin亦可。同样的问题仍是我们有哪些x该放进来?然后如何从ate到更有意义的参数是根本的识别问题也是modelling problem,这个用图难以。另外经济学家最大的一个贡献(语出Hausman)就是sem;Pearl似乎不能领会我们为何要用sem。端看pearl能不能用dag来写一个市场均衡模型. Imbens最近写了一篇review说经济学家们不用学图论 用处不多
评分图灵奖得主关于causality的科普读物。中心主题就是causality,correlation不等价于causality,因果的概念对于人来说也非常自然,这也许是因为我们的大脑是基于这样的基本概念来运作的。但是有点意外的是,根据书里描述的历史来看,人们是在最近一二十年才真正把 causality 相关的概念严格地定义出来并发展出了相关的数学工具进行演算和推理,并且这个过程似乎由于受到传统统计学“数据为王”,“correlation 为根本”的思想的无情打压和排挤,显得异常艰辛和漫长。我觉得强 AI 如果要实现的话肯定缺少不了 causality 这一环,不过要学习目前 causality 相关的理论和技术是得去看专业的书籍和论文,这本书更多的是科普,故事,和历史,当然,是很不错的一本科普
评分Not my book though
评分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。
评分Heckman, Rubin, Pearl的爱恨情仇啊。From Gelman, Pearl’s obnoxiousness obstructs the disemmination of his ideas. And works by economists are swept under the rug. 画图容易,但用Rubin亦可。同样的问题仍是我们有哪些x该放进来?然后如何从ate到更有意义的参数是根本的识别问题也是modelling problem,这个用图难以。另外经济学家最大的一个贡献(语出Hausman)就是sem;Pearl似乎不能领会我们为何要用sem。端看pearl能不能用dag来写一个市场均衡模型. Imbens最近写了一篇review说经济学家们不用学图论 用处不多
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