Causality: Model, Reasoning, and Inference

【负责】周浩杰，如有问题，欢迎交流与提出建议

【说明】本书无中文版，故目录内容是自己翻译的，所看的是英文第二版

书籍简介

这本书是因果科学领域最著名的学者之一朱迪亚·珀尔所著。它深入讨论了当代的因果分析方法，将因果科学从一个模糊的概念变成一个可以量化的理论，并可以广泛应用于数理统计、人工智能、经济学、认知科学等领域。

基本信息

• 书名 因果：模型、推理和推论 Causality: Model, Reasoning, and Inference 2nd edition
• 作者 朱迪亚·珀尔 Judea Pearl
• 出版社 剑桥大学出版社
• 出版年份 2009
• 在线网站 含有习题、勘误、问题讨论等资源

书籍目录

1. 概率、图和因果模型介绍 Introduction to Probabilities, Graphs, and Causal Models
   1. 概率论介绍 Introduction to Probability Theory
      1. 为什么需要概率 Why Probabilities?
      2. 概率论的基本概念 Basic Concepts in Probability Theory
      3. Combining Predictive and Diagnostic Supports 1.1.4 Random Variables and Expectations 8 1.1.5 Conditional Independence and Graphoids 11
   2. 图和概率 Graphs and Probabilities
2. 推断因果理论 A Theory of Inferred Causation

1 Introduction to Probabilities, Graphs, and Causal Models 1

1.1 Introduction to Probability Theory 1

1.1.1 Why Probabilities? 1

1.1.2 Basic Concepts in Probability Theory 2

1.1.3 Combining Predictive and Diagnostic Supports 6

1.1.4 Random Variables and Expectations 8

1.1.5 Conditional Independence and Graphoids 11

1.2 Graphs and Probabilities 12

1.2.1 Graphical Notation and Terminology 12

1.2.2 Bayesian Networks 13

1.2.3 The d-Separation Criterion 16

1.2.4 Inference with Bayesian Networks 20

1.3 Causal Bayesian Networks 21

1.3.1 Causal Networks as Oracles for Interventions 22

1.3.2 Causal Relationships and Their Stability 24

1.4 Functional Causal Models 26

1.4.1 Structural Equations 27

1.4.2 Probabilistic Predictions in Causal Models 30

1.4.3 Interventions and Causal Effects in Functional Models 32

1.4.4 Counterfactuals in Functional Models 33

1.5 Causal versus Statistical Terminology 38

2 A Theory of Inferred Causation 41

2.1 Introduction – The Basic Intuitions 42

2.2 The Causal Discovery Framework 43

2.3 Model Preference (Occam’s Razor) 45

2.4 Stable Distributions 48

2.5 Recovering DAG Structures 49

2.6 Recovering Latent Structures 51