Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
具體描述
讀後感
UCLA计算机系Judea Pearl教授的这本书影响非常大，因为他定义了三条重要的规则。根据这三条规则，在一定条件和因果关系结构给定的情况下，可以将将观察数据中得来的相关关系（correlation）的度量（或者说概率），转换成因果关系（causality）中的度量。 三条规则应...
評分UCLA计算机系Judea Pearl教授的这本书影响非常大，因为他定义了三条重要的规则。根据这三条规则，在一定条件和因果关系结构给定的情况下，可以将将观察数据中得来的相关关系（correlation）的度量（或者说概率），转换成因果关系（causality）中的度量。 三条规则应...
評分Kun Zhang 推荐我读的书，感觉和我的气味若合符契，这才是我喜欢的哲学书，当然你也可以说这是一本计算机科学的书。这本书并不好读，我前前后后一年半下来也就读了大约三四章，当然我花了很多时间干其他的事情，并没有全心全意地去读。很多部分我读得云山雾罩，完全不知道这一...
評分UCLA计算机系Judea Pearl教授的这本书影响非常大，因为他定义了三条重要的规则。根据这三条规则，在一定条件和因果关系结构给定的情况下，可以将将观察数据中得来的相关关系（correlation）的度量（或者说概率），转换成因果关系（causality）中的度量。 三条规则应...
評分UCLA计算机系Judea Pearl教授的这本书影响非常大，因为他定义了三条重要的规则。根据这三条规则，在一定条件和因果关系结构给定的情况下，可以将将观察数据中得来的相关关系（correlation）的度量（或者说概率），转换成因果关系（causality）中的度量。 三条规则应...
用戶評價
超級枯燥
评分A good book worth reading for anyone with math/stat background and also would love to learn causal inference. Core chapters include chap 2,3,4,7. However, the biggest pain point for me reading this book is thinking where and how I could apply the idea and approaches that I learned into my daily data science work. It's still a little bit hard for me
评分超級枯燥
评分“Data is a window to reality and data science is eyeglass that enables us to look through the window. It is not a mirror through which data looks at itself under makeup.”
评分“Data is a window to reality and data science is eyeglass that enables us to look through the window. It is not a mirror through which data looks at itself under makeup.”
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