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
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...
用户评价
购买链接：https://item.taobao.com/item.htm?spm=a1z38n.10677092.0.0.2fd21debCjUKdJ&id=574000401704
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。
评分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
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2025 qciss.net All Rights Reserved. 小哈图书下载中心 版权所有