Elements of Causal Inference: Foundations and Learning Algorithms
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
书名：Elements of Causal Inference: Foundations and Learning Algorithms
作者：Judea Pearl/Madelyn Glymour/Nicholas P. Jewell
中文翻译版：统计因果推理入门
译者: 杨矫云 / 安宁 / 李廉
作者介绍
Judea Pearl是美国加利福尼亚大学洛杉矶分校计算机科学和统计学的教授，他是认知系统实验室负责人，从事人工智能、因果推理和科学哲学的研究。Pearl 是《因果推理期刊》的联合创始人和编辑，著有三本关于推理领域的里程碑式著作。他的著作《因果关系：模型、推理和推断》（剑桥出版社，2000，2009）介绍了现代因果分析中使用的许多方法。该书获得了伦敦经济学院拉卡托斯奖（ Lakatos Award），并被超过13000篇科学出版物引用。Pearl 是美国国家科学院院士、美国国家工程院院士、美国人工智能协会的创始会士。由于对概率和因果推理的基础性贡献，Pearl 囊获了包括以色列理工学院的哈维奖（Harvey Prize）和 ACM 图灵奖在内的众多奖项。
Madelyn Glymour是卡内基-梅隆大学的数据分析师、加利福尼亚大学洛杉矶分校认知系统实验室的科学作家和编辑。研究兴趣在因果发现以及能够让广大读者理解复杂概念所蕴含的艺术。
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 PetersJonas Peters is Professor of Statistics at the University of Copenhagen. Dominik JanzingDominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. Bernhard SchölkopfBernhard 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
Preface xi
1 Preliminaries: Statistical and Causal Models 1
1.1 Why Study Causation 1
1.2 Simpson’s Paradox 2
1.3 Probability and Statistics 9
1.3.1 Variables 10
1.3.2 Events 11
1.3.3 Conditional probability 11
1.3.4 Independence 13
1.3.5 Probability distributions 14
1.3.6 The law of total probability 15
1.3.7 Using Bayes’ rule 18
1.3.8 Expected values 22
1.3.9 Variance and covariance 24
1.3.10 Regression 27
1.3.11 Multiple regression 31
1.4 Graphs 33
1.5 Structural Causal Models 36
1.5.1 Modeling causal assumptions 36
1.5.2 Product decomposition 40
2 Graphical Models and Their Applications 47
2.1 Connecting Models to Data 47
2.2 Chains and Forks 48
2.3 Colliders 55
2.4 d-Separation 62
2.5 Model Testing and Causal Search 66
3 The Effects of Interventions 71
3.1 Interventions 71
3.2 The Adjustment Formula 74
3.2.1 To adjust or not to adjust? 79
3.2.2 Multiple interventions and the truncated product rule 81
3.3 The Back-Door Criterion 82
3.4 The Front-Door Criterion 89
3.5 Conditional Interventions and Covariate-Specific Effects 95
3.6 Inverse Probability Weighing 98
3.7 Mediation 103
3.8 Causal Inference in Linear Systems 107
3.8.1 Structural vs. regression coefficients 110
3.8.2 The causal interpretation of structural coefficients 111
3.8.3 Identifying structural coefficients and causal effect 113
3.8.4 Mediation in linear systems 119
4 Counterfactuals and their Applications 123
4.1 Counterfactuals 123
4.2 Defining and Computing Counterfactuals 126
4.2.1 The structural interpretation of counterfactuals 126
4.2.2 The fundamental law of counterfactuals 130
4.2.3 From population data to individual behavior – an illustration 131
4.2.4 The three steps in computing counterfactuals 133
4.3 Non-Deterministic Counterfactuals 136
4.3.1 Probabilities of counterfactuals 136
4.3.2 The Graphical representation of counterfactuals 141
4.3.3 Counterfactuals in experimental settings 144
4.3.4 Counterfactuals in linear models 147
4.4 Practical uses of counterfactuals 149
4.4.1 Recruitment to a program 149
4.4.2 Additive interventions 152
4.4.3 Personal decision making 155
4.4.4 Sex discrimination in hiring 158
4.4.5 Mediation and path-disabling interventions 159
4.5 Mathematical Tool Kits for Attribution and Mediation 161
4.5.1 A tool kit for attribution and probabilities of causation 162
4.5.2 A tool kit for mediation 167
References 176
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豆瓣书评
一次减法带来的科学革命
“一直都不太看好所谓的归因分析, 也不太理解为什么归因分析会作为图灵奖评审标准. 归因分析其实就是概率减法而已, 这是个再简单不过的概念.归因分析近年来受到了互联网公司的关注. Uber 就是其中之一.理论科学有的时候只要稍稍有一些创新概念, 就会带来应用领域的革命, 所以不要放过自己每一次的灵光一现.” ——Hao
Goodreads书评
“I read this book because I was so impressed with the importance of causal inferencing from data, as Pearl has developed it, in his more popular book, "The Book of Why". But that book didn't present enough detail for me to actually apply causal analysis to my own data. I hoped that a self-styled "Primer" would fill that gap.
Unfortunately, this book did not help......” ——James Foster
“......So yes, I guess I'm drinking the Pearl Kool-aid. This book deserves such praise in my opinion, though, for having the informative lack of fluff of a textbook without the soporific dryness of one” ——Anthony DiGiovanni
集智俱乐部读书会推荐
因果推理与机器学习读书会
大数据时代的下一场变革——因果革命正在酝酿之中，通过融合因果推理和机器学习而构建出来的Causal AI系统，有望奠定强人工智能的基石。集智俱乐部联合北京智源人工智能研究院，邀请了一批对因果科学与Casual AI感兴趣的研究者，开展为期2-3个月的系列线上读书会，研读经典和前沿论文，并尝试集体撰写一部书籍。
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