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Elements of Causal Inference: Foundations and Learning Algorithms (查看源代码)
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[[文件:《统计因果推理入门》.jpg|缩略图|《统计因果推理入门》]]
[[文件:《统计因果推理入门》.jpg|缩略图|《统计因果推理入门》]]
=内容简介=
=内容简介-英文=
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
=内容简介-中文=
《因果推理：基础与学习算法》从概率统计的角度入手，分析了因果推理的假设，揭示这些假设所暗示的因果推理和学目的。本书分别论述了变量和多变量情况下的因果模型、学习因果模型及其与机器学关系，讨论了因果推理隐藏变量有关的问题、时间系列的因果分析。 《因果推理：基础与学习算法》可作为高等院校人工智能和计算机科学等相关专业高年级本科生和硕士研究生的教材，也可供研究机器学习、因果推理的技术人员参考。
=基本信息=
=基本信息=
书名：Elements of Causal Inference: Foundations and Learning Algorithms
作者：Judea Pearl/Madelyn Glymour/Nicholas P. Jewell
作者：Judea Pearl/Madelyn Glymour/Nicholas P. Jewell
'''Nicholas P. Jewell'''是加利福尼亚大学伯克利分校生物统计学和统计学教授。自从1981年进入伯克利分校后，先后担任多个学术和行政职务，特别是1994-2000年间担任了副教务长。他还曾在爱丁堡大学、牛津大学、伦敦卫生与热带医学院以及京都大学担任学术职务。2007年，他在意大贝拉吉奥洛克菲勒基金研究中心做访问学者。Jewell 是美国统计协会、数理统计研究所和美国科学促进协会（AAAS）会士。曾经获得斯内德克奖和哈佛大学统计科学马文·泽伦领导奖。Jewell 目前是《美国统计协会会刊：理论与方法》杂志编辑、美国科学促进会统计学分会的主席。他的研究重点是把统计方法应用到传染病和慢性病流行病学、药物安全性评估、比较分析和人权领域。
'''Nicholas P. Jewell'''是加利福尼亚大学伯克利分校生物统计学和统计学教授。自从1981年进入伯克利分校后，先后担任多个学术和行政职务，特别是1994-2000年间担任了副教务长。他还曾在爱丁堡大学、牛津大学、伦敦卫生与热带医学院以及京都大学担任学术职务。2007年，他在意大贝拉吉奥洛克菲勒基金研究中心做访问学者。Jewell 是美国统计协会、数理统计研究所和美国科学促进协会（AAAS）会士。曾经获得斯内德克奖和哈佛大学统计科学马文·泽伦领导奖。Jewell 目前是《美国统计协会会刊：理论与方法》杂志编辑、美国科学促进会统计学分会的主席。他的研究重点是把统计方法应用到传染病和慢性病流行病学、药物安全性评估、比较分析和人权领域。
'''Jonas Peters'''Jonas Peters is Professor of Statistics at the University of Copenhagen.
'''Dominik Janzing'''Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.
'''Bernhard Schölkopf'''Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
=内容目录=
=内容目录=
Preface xi
Preface xi