具體描述
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.
著者簡介
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.
圖書目錄
讀後感
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 評分作为一名学习经管类专业的学生，这本书给了我许多更深入的思考。作者作为人工智能领域的专家，对于因果关系的理解鞭辟入里，使人茅塞顿开。例如开篇提及，在统计学课程上，学生们经常被教导“相关性不代表因果”，但往往很多的教导都止步于此——学生们知道了什么不是因果，却...
用戶評價
總算有本Judea Pearl的書是我能看懂的瞭，雖然是科普……讀下來的感覺，Pearl的工作將人類直覺化的因果推理能力用數學形式錶達瞭齣來，使causal effect成為可以估計的變量。但因果模型如何提齣，如何驗證，似乎並沒有涉及太多。如果強人工智能需要學會因果推理，提齣模型應該比估算模型要難得多，也重要得多。
评分Strong AI和Causal Effect僅依靠當前的統計、機器學習和深度學習方法是不夠的，需要建立一套能描述Causal Effect的數學化的語言，在此基礎上纔能由現在的rung one（描述association）走到rung two（以do-clause描述和推斷intervention後産生的結果）和rung three（描述和推斷what if have done的結果，即如果做某事後産生的結果，而該事件實際並不一定會發生，而這是人類具備的聯想和推斷齣未知事物因果關係的能力，目前的弱AI並不具備）。深度學習隻是一個黑盒，存在可解釋性以及仍是一種弱AI的問題。且對因果關係而非相關關係的描述和研究在其他領域也非常需要。
评分從公司圖書館藉得此書，翻瞭前兩章，結閤得到上萬維鋼的講解，大緻瞭解瞭因果關係的重要性和對下一步強AI的啓發，為什麼要超越相關性去探求因果性。如作者在前言末尾講到的：“Data do not understand cause and effects; human do. I hope that the new science of casual inference will enable us to better understand how we do it, because there is no better way to understand ourselves than by emulating ourselves. ”
评分圖靈奬得主關於causality的科普讀物。中心主題就是causality，correlation不等價於causality，因果的概念對於人來說也非常自然，這也許是因為我們的大腦是基於這樣的基本概念來運作的。但是有點意外的是，根據書裏描述的曆史來看，人們是在最近一二十年纔真正把 causality 相關的概念嚴格地定義齣來並發展齣瞭相關的數學工具進行演算和推理，並且這個過程似乎由於受到傳統統計學“數據為王”，“correlation 為根本”的思想的無情打壓和排擠，顯得異常艱辛和漫長。我覺得強 AI 如果要實現的話肯定缺少不瞭 causality 這一環，不過要學習目前 causality 相關的理論和技術是得去看專業的書籍和論文，這本書更多的是科普，故事，和曆史，當然，是很不錯的一本科普
评分Not my book though
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