Causality: Models, Reasoning and Inference (Hardcover)

Judea Pearl

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商品描述

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. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 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 interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

商品描述(中文翻譯)

由該領域的頂尖研究者撰寫,本書全面介紹了現代因果分析。它展示了因果性如何從模糊的概念發展成為一個具有顯著應用價值的數學理論,涉及統計學、人工智能、經濟學、哲學、認知科學以及健康和社會科學等領域。Judea Pearl介紹並統一了概率、操作、反事實和結構方法來研究因果關係,並提出了簡單的數學工具來研究因果關聯和統計關聯之間的關係。本書將為統計學、人工智能、商業、流行病學、社會科學和經濟學的標準課程中納入因果分析鋪平道路。這些領域的學生將找到自然模型、簡單的推論程序以及對因果概念的精確數學定義,而這些傳統教材往往避而不談或使其過於複雜。《因果性》的第一版在統計學、哲學、計算機科學、社會科學和經濟學中引起了範式轉變。它被引用在超過5,000篇科學出版物中,繼續使科學家擺脫傳統的統計思維模式。在這個修訂版中,Judea Pearl闡明了棘手的問題,回答了讀者的問題,並全面介紹了這一研究領域的最新進展。《因果性》將對各個領域的學生和專業人士感興趣。任何希望從數據中闡明有意義的關係、預測行動和政策的影響、評估報告事件的解釋或形成對因果理解和因果言論的理論的人都會發現本書具有刺激性和無價值。