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第127行： − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − '''3.5 Graphical Tests of Identifiability 89'''+ + − + − + − '''3.6 Discussion 94'''+ − + − + − + − + − === 4 Actions, Plans, and Direct Effects 107 ===+ − '''4.1 Introduction 108''' − + + − + − + − '''4.2 Conditional Actions and Stochastic Policies 113'''+ − + − + − + − + − + − '''4.4 The Identification of Dynamic Plans 118'''+ − + − + − + − + − '''4.5 Direct and Indirect Effects 126'''+ − + − + − + − + − + − === 5 Causality and Structural Models in Social Science and Economics 133 ===+ − '''5.1 Introduction 134''' − + + − + − + − '''5.2 Graphs and Model Testing 140'''+ − + − + − + − '''5.3 Graphs and Identifiability 149'''+ − + − + − + − '''5.4 Some Conceptual Underpinnings 159'''+ − + − + − + − '''5.5 Conclusion 170'''+ − + − + − + − + − + − + + + 第277行：
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目录翻译到第五章
== 基本信息 ==
== 基本信息 ==
* '''书名''' 因果：模型、推理和推论 Causality: Model, Reasoning, and Inference 2nd edition
* '''书名''' 因果论：模型、推理和推断 Causality: Model, Reasoning, and Inference 2nd edition
* '''作者''' [https://wiki.swarma.org/index.php/Judea_Pearl 朱迪亚·珀尔 Judea Pearl]
* '''作者''' [https://wiki.swarma.org/index.php/Judea_Pearl 朱迪亚·珀尔 Judea Pearl]
* '''出版社''' 剑桥大学出版社
* '''出版社''' 剑桥大学出版社
* 与断言逻辑相比，基于概率的表达更容易处理，不然断言需要考虑到大量使其不成立的特例。
* 与断言逻辑相比，基于概率的表达更容易处理，不然断言需要考虑到大量使其不成立的特例。
1.1.2 概率论中的基本概念 Basic Concepts in Probability Theory 2
1.1.2 概率论的基本概念 Basic Concepts in Probability Theory 2
* 介绍有关概率论中离散变量的相关基础知识，并主要聚焦于贝叶斯推理。
* 介绍有关概率论中离散变量的相关基础知识，并主要聚焦于贝叶斯推理。
* 因果贝叶斯网络的定义和两个性质
* 因果贝叶斯网络的定义和两个性质
1.3.2 因果关系和它们的稳定性 Causal Relationships and Their Stability 24
1.3.2 因果关系及其稳定性 Causal Relationships and Their Stability 24
* 说明了因果关系为何比概率关系稳定，因果关系的重要性。
* 说明了因果关系为何比概率关系稳定，因果关系的重要性。
* 对比了统计学与因果科学术语的差异
* 对比了统计学与因果科学术语的差异
=== 2 推断因果理论 A Theory of Inferred Causation 41 ===
=== 2 因果推断理论 A Theory of Inferred Causation 41 ===
'''2.1 绪论—直观的理解 Introduction – The Basic Intuitions 42'''
'''2.1 绪论：直观的理解 Introduction – The Basic Intuitions 42'''
'''2.2 因果发现框架 The Causal Discovery Framework 43'''
'''2.2 因果发现框架 The Causal Discovery Framework 43'''
'''2.4 稳定分布 Stable Distributions 48'''
'''2.4 稳定分布 Stable Distributions 48'''
* 为什么需要提出稳定性这个概念。最小性原则不能保证模型是最小的或是计算可行的【看着有点怪，具体最小是什么意思我也不是非常懂，这句话的意思出自这里Although the minimality principle is sufficient for forming a normative theory of inferred causation, it does not guarantee that the structure of the actual data-generating model would be minimal, or that the search through the vast space of minimal structures would be computationally practical】
* 为什么需要提出稳定性这个概念。最小性原则不能保证模型是最小的或是计算可行的【看着有点怪，具体最小是什么意思我也不是非常懂，这句话的意思出自这里Although the minimality principle is sufficient for forming a normative theory of inferred causation, it does not guarantee that the structure of the actual data-generating model would be minimal, or that the search through the vast space of minimal structures would be computationally practical】
* 介绍稳定性的定义，阐释其与最小性间的关系
* 介绍稳定性的定义，阐释其与最小性间的关系
* 潜在因果，真实因果，伪相关，有时间信息的真实因果，有时间信息的伪相关这些概念的定义
* 潜在因果，真实因果，伪相关，有时间信息的真实因果，有时间信息的伪相关这些概念的定义
'''2.8 Nontemporal Causation and Statistical Time 57'''
'''2.8 非时间因果与统计时间 Nontemporal Causation and Statistical Time 57'''
*
*
'''2.9 总结 Conclusions 59'''
'''2.9 总结 Conclusions 59'''
2.9.1 On Minimality, Markov, and Stability 61
2.9.1 关于极小性，马尔可夫性和稳定性 On Minimality, Markov, and Stability 61
=== 3 Causal Diagrams and the Identification of Causal Effects 65 ===
=== 3 因果图和识别因果效应 Causal Diagrams and the Identification of Causal Effects 65 ===
'''3.1 Introduction 66'''
'''3.1 简介 Introduction 66'''
'''3.2 Intervention in Markovian Models 68'''
'''3.2 马尔可夫模型中的干预 Intervention in Markovian Models 68'''
3.2.1 Graphs as Models of Interventions 68
3.2.1 作为干预模型的图 Graphs as Models of Interventions 68
3.2.2 Interventions as Variables 70
3.2.2 作为干预的变量 Interventions as Variables 70
3.2.3 Computing the Effect of Interventions 72
3.2.3 计算干预的效应 Computing the Effect of Interventions 72
3.2.4 Identification of Causal Quantities 77
3.2.4 识别因果量值 Identification of Causal Quantities 77
'''3.3 Controlling Confounding Bias 78'''
'''3.3 控制混杂偏差 Controlling Confounding Bias 78'''
3.3.1 The Back-Door Criterion 79
3.3.1 后门准则 The Back-Door Criterion 79
3.3.2 The Front-Door Criterion 81
3.3.2 前门准则 The Front-Door Criterion 81
3.3.3 Example: Smoking and the Genotype Theory 83
3.3.3 例子：吸烟和基因论 Example: Smoking and the Genotype Theory 83
'''3.4 A Calculus of Intervention 85'''
'''3.4 计算干预 A Calculus of Intervention 85'''
3.4.1 Preliminary Notation 85
3.4.1 记号预备 Preliminary Notation 85
3.4.2 Inference Rules 85
3.4.2 推理规则 Inference Rules 85
3.4.3 Symbolic Derivation of Causal Effects: An Example 86
3.4.3 例子：因果效应的符号推导 Symbolic Derivation of Causal Effects: An Example 86
3.4.4 Causal Inference by Surrogate Experiments 88
3.4.4 替代试验的因果推断 Causal Inference by Surrogate Experiments 88
* 由于一些原因如成本或伦理问题，不能控制某变量进行实验，于是需要控制另一个可替代的变量
* 介绍利用替代变量进行因果效应的计算方法
3.5.1 Identifying Models 91
'''3.5 可识别性的图测试 Graphical Tests of Identifiability 89'''
3.5.2 Nonidentifying Models 93
3.5.1 可识别模型 Identifying Models 91
3.5.2 不可识别模型 Nonidentifying Models 93
3.6.1 Qualifications and Extensions 94
'''3.6 讨论 Discussion 94'''
3.6.2 Diagrams as a Mathematical Language 96
3.6.1 要求与扩展 Qualifications and Extensions 94
3.6.3 Translation from Graphs to Potential Outcomes 98
3.6.2 作为数学语言的图 Diagrams as a Mathematical Language 96
3.6.4 Relations to Robins’s G-Estimation 102
3.6.3 从图到潜在因果的转换 Translation from Graphs to Potential Outcomes 98
3.6.4 跟Robin的G-估计的关系 Relations to Robins's G-Estimation 102
4.1.1 Actions, Acts, and Probabilities 108
=== 4 行动，计划和直接效应 Actions, Plans, and Direct Effects 107 ===
'''4.1 简介 Introduction 108'''
4.1.2 Actions in Decision Analysis 110
4.1.1 行动，动作和概率 Actions, Acts, and Probabilities 108
4.1.3 Actions and Counterfactuals 112
4.1.2 决策分析中的行动 Actions in Decision Analysis 110
4.1.3 行动和反事实 Actions and Counterfactuals 112
'''4.3 When Is the Effect of an Action Identifiable? 114'''
'''4.2 有条件行动和随机策略 Conditional Actions and Stochastic Policies 113'''
4.3.1 Graphical Conditions for Identification 114
'''4.3 什么时候行动的结果是可测量的 When Is the Effect of an Action Identifiable? 114'''
4.3.2 Remarks on Efficiency 116
4.3.1 基于图的识别条件 Graphical Conditions for Identification 114
4.3.3 Deriving a Closed-Form Expression for Control Queries 117
4.3.2 识别效率 Remarks on Efficiency 116
4.3.4 Summary 118
4.3.3 对控制问题解析解的推到 Deriving a Closed-Form Expression for Control Queries 117
4.3.4 总结 Summary 118
4.4.1 Motivation 118
'''4.4 动态计划的识别 The Identification of Dynamic Plans 118'''
4.4.2 Plan Identification: Notation and Assumptions 120
4.4.1 动机 Motivation 118
4.4.3 Plan Identification: The Sequential Back-Door Criterion 121
4.4.2 识别计划：记号和假设 Plan Identification: Notation and Assumptions 120
4.4.4 Plan Identification: A Procedure 124
4.4.3 识别计划：顺序后门准则 Plan Identification: The Sequential Back-Door Criterion 121
4.4.4 识别计划：流程 Plan Identification: A Procedure 124
4.5.1 Direct versus Total Effects 126
'''4.5 直接和间接效应 Direct and Indirect Effects 126'''
4.5.2 Direct Effects, Definition, and Identification 127
4.5.1 直接效应和总效应 Direct versus Total Effects 126
4.5.3 Example: Sex Discrimination in College Admission 128
4.5.2 直接效益，定义和识别 Direct Effects, Definition, and Identification 127
4.5.4 Natural Direct Effects 130
4.5.3 例子：大学录取中的性别歧视 Example: Sex Discrimination in College Admission 128
4.5.5 Indirect Effects and the Mediation Formula 132
4.5.4 自然直接效应 Natural Direct Effects 130
4.5.5 间接效应和中介公式 Indirect Effects and the Mediation Formula 132
5.1.1 Causality in Search of a Language 134
=== 5 社会学和经济学中的因果关系和结构模型 Causality and Structural Models in Social Science and Economics 133 ===
'''5.1 简介 Introduction 134'''
5.1.2 SEM: How Its Meaning Became Obscured 135
5.1.1 寻找因果语言 Causality in Search of a Language 134
5.1.3 Graphs as a Mathematical Language 138
5.1.2 SEM：意思是怎么变模糊的 SEM: How Its Meaning Became Obscured 135
5.1.3 作为数学语言的图 Graphs as a Mathematical Language 138
5.2.1 The Testable Implications of Structural Models 140
'''5.2 图和模型测试 Graphs and Model Testing 140'''
5.2.2 Testing the Testable 144
5.2.1 结构模型的可检验含义 The Testable Implications of Structural Models 140
5.2.3 Model Equivalence 145
5.2.2 检验和可检验性 Testing the Testable 144
5.2.3 模型等价 Model Equivalence 145
5.3.1 Parameter Identification in Linear Models 149
'''5.3 图和可识别性 Graphs and Identifiability 149'''
5.3.2 Comparison to Nonparametric Identification 154
5.3.1 线性模型的参数识别 Parameter Identification in Linear Models 149
5.3.3 Causal Effects: The Interventional Interpretation of Structural Equation Models 157
5.3.2 对比非参数识别 Comparison to Nonparametric Identification 154
5.3.3 因果效应：结构等式模型的干预解释 Causal Effects: The Interventional Interpretation of Structural Equation Models 157
5.4.1 What Do Structural Parameters Really Mean? 159
'''5.4 部分基础概念 Some Conceptual Underpinnings 159'''
5.4.2 Interpretation of Effect Decomposition 163
5.4.1 结构参数的真正含义是什么？ What Do Structural Parameters Really Mean? 159
5.4.3 Exogeneity, Superexogeneity, and Other Frills 165
5.4.2 效应分解的解释 Interpretation of Effect Decomposition 163
5.4.3 外生性，超外生性和其他 Exogeneity, Superexogeneity, and Other Frills 165
'''5.6 Postscript for the Second Edition 171'''
'''5.5 结论 Conclusion 170'''
5.6.1 An Econometric Awakening? 171
'''5.6 第二版附言 Postscript for the Second Edition 171'''
5.6.2 Identification in Linear Models 171
5.6.1 计量经济学的觉醒 An Econometric Awakening? 171
5.6.3 Robustness of Causal Claims 172
5.6.2 线性模型的识别问题 Identification in Linear Models 171
=== 6 Simpson’s Paradox, Confounding, and Collapsibility 173 ===
5.6.3 因果论断的鲁棒性 Robustness of Causal Claims 172
'''6.1 Simpson’s Paradox: An Anatomy 174'''
=== 6 Simpson's Paradox, Confounding, and Collapsibility 173 ===
'''6.1 Simpson's Paradox: An Anatomy 174'''
6.1.1 A Tale of a Non-Paradox 174
6.1.1 A Tale of a Non-Paradox 174
6.2.1 Introduction 182
6.2.1 Introduction 182
6.2.2 Causal and Associational Definitions 184
6.2.2 Causal and Associational Definitions 184
'''6.3 How the Associational Criterion Fails 185'''
'''6.3 How the Associational Criterion Fails 185'''
6.3.1 Failing Sufficiency via Marginality 185
6.3.1 Failing Sufficiency via Marginality 185
6.3.2 Failing Sufficiency via Closed-World Assumptions 186
6.3.2 Failing Sufficiency via Closed-World Assumptions 186
6.3.3 Failing Necessity via Barren Proxies 186
6.3.3 Failing Necessity via Barren Proxies 186
6.4.1 Motivation 189
6.4.1 Motivation 189
6.4.2 Formal Definitions 191
6.4.2 Formal Definitions 191
6.4.3 Operational Test for Stable No-Confounding 192
6.4.3 Operational Test for Stable No-Confounding 192
'''7.1 Structural Model Semantics 202'''
'''7.1 Structural Model Semantics 202'''
7.1.1 Definitions: Causal Models, Actions, and Counterfactuals 202
7.1.1 Definitions: Causal Models, Actions, and Counterfactuals 202
7.1.2 Evaluating Counterfactuals: Deterministic Analysis 207
7.1.2 Evaluating Counterfactuals: Deterministic Analysis 207
7.4.4 Relations to the Neyman–Rubin Framework 243
7.4.4 Relations to the Neyman–Rubin Framework 243
7.4.5 Exogeneity and Instruments: Counterfactual and Graphical Definitions 245
7.4.5 Exogeneity and Instruments: Counterfactual and Graphical Definitions 245
'''7.5 Structural versus Probabilistic Causality 249'''
'''7.5 Structural versus Probabilistic Causality 249'''
'''8.6 Conclusion 281'''
'''8.6 Conclusion 281'''
=== 9 Probability of Causation: Interpretation and Identification 283 ===
=== 9 Probability of Causation: Interpretation and Identification 283 ===
'''9.1 Introduction 283'''
'''9.1 Introduction 283'''
'''9.2 Necessary and Sufficient Causes: Conditions of Identification 286'''
'''9.2 Necessary and Sufficient Causes: Conditions of Identification 286'''
9.2.1 Definitions, Notation, and Basic Relationships 286
9.2.1 Definitions, Notation, and Basic Relationships 286
9.2.2 Bounds and Basic Relationships under Exogeneity 289
9.2.2 Bounds and Basic Relationships under Exogeneity 289
9.2.3 Identifiability under Monotonicity and Exogeneity 291
9.2.3 Identifiability under Monotonicity and Exogeneity 291
9.2.4 Identifiability under Monotonicity and Nonexogeneity 293
9.2.4 Identifiability under Monotonicity and Nonexogeneity 293
'''9.3 Examples and Applications 296'''
'''9.3 Examples and Applications 296'''
9.3.5 Summary of Results 303
9.3.5 Summary of Results 303
'''9.4 Identification in Nonmonotonic Models 304'''
'''9.4 Identification in Nonmonotonic Models 304'''
'''9.5 Conclusions 307'''
'''9.5 Conclusions 307'''
=== 10 The Actual Cause 309 ===
=== 10 The Actual Cause 309 ===
'''10.1 Introduction: The Insufficiency of Necessary Causation 309'''
'''10.1 Introduction: The Insufficiency of Necessary Causation 309'''
10.1.1 Singular Causes Revisited 309
10.1.1 Singular Causes Revisited 309
'''10.3 Causal Beams and Sustenance-Based Causation 318'''
'''10.3 Causal Beams and Sustenance-Based Causation 318'''
10.3.1 Causal Beams: Definitions and Implications 318
10.3.1 Causal Beams: Definitions and Implications 318
10.3.2 Examples: From Disjunction to General Formulas 320
10.3.2 Examples: From Disjunction to General Formulas 320
'''11.5 Causal Analysis in Linear Structural Models 366'''
'''11.5 Causal Analysis in Linear Structural Models 366'''
11.5.1 General Criterion for Parameter Identification (Chapter 5, pp. 149–54) 366
11.5.1 General Criterion for Parameter Identification (Chapter 5, pp. 149–54) 366
11.5.2 The Causal Interpretation of Structural Coefficients 366
11.5.2 The Causal Interpretation of Structural Coefficients 366
11.5.3 Defending the Causal Interpretation of SEM (or, SEM Survival Kit) 368
11.5.3 Defending the Causal Interpretation of SEM (or, SEM Survival Kit) 368
11.6.1 Simpson's Paradox and Decision Trees 380
11.6.1 Simpson's Paradox and Decision Trees 380
11.6.2 Is Chronological Information Sufficient for Decision Trees? 382
11.6.2 Is Chronological Information Sufficient for Decision Trees? 382
11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384
11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384