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2024-05-17
The Book of Why 2024 pdf epub mobi 電子書 下載
這本書著實燒腦,是講因果關係的新科學,我實在不能用簡明的語言來描述,要舉的案例也有點冗長,我隻能告訴你幾個大的框架: 1 三級因果思維,原來我們的思想還能分齣個三個層次,分彆是觀察,乾預和想象,現在的人工智能還隻達到第一級,大數據階段[發呆] 2 迴歸均值,你知道...
評分 評分 評分“20世紀50年代末60年代初,統計學傢和醫生就整個20世紀最引人注目的一個醫學問題産生瞭意見衝突:吸煙會導緻肺癌嗎?在這場辯論過去瞭半個世紀之後的現在,我們認為答案是理所當然的。但在當時,這個問題完全處於迷霧之中。” 01 — 書比較厚,正文346頁,注釋26頁。內容也相對硬核...
圖書標籤: 人工智能 統計學 因果論 Causality 計算機 Statistics AI 思維
真是打開瞭新世界的大門,本以為讀瞭一些計量經濟學和實驗設計的東西已經算入門瞭,沒想到可能是大傢都走錯門瞭,甚至門在哪裏可能都還沒達成一緻
評分Rubin在課上還噴瞭Judea一頓,錶示看不慣這種拿概率圖建模的方法。。不過do calculus如何隻從數據中得到因果關係確實有趣。關於因果推斷的模型到底哪個纔是有用的,見仁見智吧
評分真是打開瞭新世界的大門,本以為讀瞭一些計量經濟學和實驗設計的東西已經算入門瞭,沒想到可能是大傢都走錯門瞭,甚至門在哪裏可能都還沒達成一緻
評分科學法是貝葉斯定理的一次應用。因果圖形式化因果結構,do算子對有嚮無環圖中指嚮X的有嚮邊全部切斷。由於變量不能全部觀測,用前門準則來控製無法觀察到的混雜因素,與RCT目標一緻;若變量集閤Z相對於(X,Y)滿足後門準則,則X到Y因果可識彆。感覺這些都是對相關性不能解決以及解決起來復雜的問題透明優化。反事實算法則擴寬數據解答問題的範圍,NIE形式化間接影響。結構因果模型很大的一個優點就是對於綫性非綫性函數、離散或連續變量都有效。作者太賣關子,前幾章講統計學史,舊故事很多,7-9章是乾貨。思路是經典宏觀實踐的,因果哲學講得很淺。但是應用領域極為廣泛,畢竟是對相關性大改良,文科也能用呐。不知道因果模型處理相互乾涉和疊加態什麼的會怎麼樣。可能要讀Causality一書纔能深刻瞭解本書數學化的嚴格證明。
評分好書,很感興趣的topic,比之前翻得兩本Pearl的書還是好懂多瞭。因果關係這種我們平時最習以為常的東西卻遠遠瞭解得不夠,想起之前一個同學做得就是qft裏麵的因果律,這個話題遠遠不隻是哲學上的,許多日常的案例都會用到這些。
The Book of Why 2024 pdf epub mobi 電子書 下載