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.
發表於2025-04-27
The Book of Why 2025 pdf epub mobi 電子書 下載
The ladder of causation Association Predictions based on passive observations Intervention Involving not just seeing but changing what is Counterfactuals Not only experiments, but also need the model of the underlying causal process--"theory" or "a law of n...
評分 評分 評分2016年,在大數據的幫助下,人工智能(AI)圍棋軟件AlphaGo在係列賽中以4:1戰勝瞭世界圍棋頂尖高手李世石,震驚瞭全人類。 當時網絡上有人戲謔道:“人工智能贏瞭不可怕,至少說明它還不懂得韜光隱晦,如果它假裝輸給人類,那纔更加可怕。”這句看似戲言的話,卻暗藏瞭人工智能...
圖書標籤: 人工智能 統計學 因果論 Causality 計算機 Statistics AI 思維
7-9章比較難懂,看來太前沿,不適閤非學界人士,簡單瞭解一下吧,再過兩年深度學習碰壁以後估計會迴歸因果分析~實現強智能應該繞不開因果體係
評分Satisfied and recommended to colleagues already...
評分這是@ 木遙 推薦我去聽的書,因為他想知道書裏所寫的東西(基於因果關係模型來分析數據和作齣結論)在我的領域裏(epidemiology),究竟是新東西還是老生常談。我的讀後看法1)不是新東西,但本領域也有很多很多研究者並未很好地使用這些原則,哪怕很多原則其實是epid 101內容;2)最近幾十年總的來說還是再越變越好,更多人開始主動運用這些原則;3)作者對傳統統計的批判我不是特彆贊同,但我也不不是統計學傢;4)作者建立發展的那些計算方法並沒有常規地運用在我的領域裏,但我覺得很有意思,可以多瞭解一些;5)前一半可以作為我research methods這門課的課本推薦給學生。總之還是挺好一本書,雖然對於我來講新東西不算多。
評分看的很過癮,對頻率學派和貝葉斯學派都有反思
評分感覺寫得不好. 如果要瞭解causal inference還是看標準的教材吧
The Book of Why 2025 pdf epub mobi 電子書 下載