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翻译目录到11.4.4
'''8.6 结论 Conclusion 281'''
'''8.6 结论 Conclusion 281'''
=== 9 Probability of Causation: Interpretation and Identification 283 ===
=== 9 因果概率：解释和识别 Probability of Causation: Interpretation and Identification 283 ===
'''9.1 Introduction 283'''
'''9.1 简介 Introduction 283'''
'''9.2 Necessary and Sufficient Causes: Conditions of Identification 286'''
'''9.2 必要和充分因果：条件和识别 Necessary and Sufficient Causes: Conditions of Identification 286'''
9.2.1 Definitions, Notation, and Basic Relationships 286
9.2.1 定义，记号和基本关系 Definitions, Notation, and Basic Relationships 286
9.2.2 Bounds and Basic Relationships under Exogeneity 289
9.2.2 外生性下的边界和基本关系 Bounds and Basic Relationships under Exogeneity 289
9.2.3 Identifiability under Monotonicity and Exogeneity 291
9.2.3 单调性和外生性下的可识别性 Identifiability under Monotonicity and Exogeneity 291
9.2.4 Identifiability under Monotonicity and Nonexogeneity 293
9.2.4 单调性和非外生性下的可识别性 Identifiability under Monotonicity and Nonexogeneity 293
'''9.3 Examples and Applications 296'''
'''9.3 例子和应用 Examples and Applications 296'''
9.3.1 Example 1: Betting against a Fair Coin 296
9.3.1 例1：公平硬币赌博 Example 1: Betting against a Fair Coin 296
9.3.2 Example 2: The Firing Squad 297
9.3.2 例2：刑法执行 Example 2: The Firing Squad 297
9.3.3 Example 3: The Effect of Radiation on Leukemia 299
9.3.3 例3：辐射对白血病的影响 Example 3: The Effect of Radiation on Leukemia 299
9.3.4 Example 4: Legal Responsibility from Experimental and Nonexperimental Data 302
9.3.4 例4：来自实验数据和非实验数据的合法责任 Example 4: Legal Responsibility from Experimental and Nonexperimental Data 302
9.3.5 Summary of Results 303
9.3.5 结果总结 Summary of Results 303
'''9.4 Identification in Nonmonotonic Models 304'''
'''9.4 识别非单调模型 Identification in Nonmonotonic Models 304'''
'''9.5 Conclusions 307'''
'''9.5 总结 Conclusions 307'''
=== 10 The Actual Cause 309 ===
=== 10 实际原因 The Actual Cause 309 ===
'''10.1 Introduction: The Insufficiency of Necessary Causation 309'''
'''10.1 简介：必要因果的不充分性 Introduction: The Insufficiency of Necessary Causation 309'''
10.1.1 Singular Causes Revisited 309
10.1.1 重新回顾单原因 Singular Causes Revisited 309
10.1.2 Preemption and the Role of Structural Information 311
10.1.2 抢占和结构信息的作用 Preemption and the Role of Structural Information 311
10.1.3 Overdetermination and Quasi-Dependence 313
10.1.3 过度确定和伪依赖性 Overdetermination and Quasi-Dependence 313
10.1.4 Mackie's INUS Condition 313
10.1.4 麦基的INUS条件 Mackie's INUS Condition 313
'''10.2 Production, Dependence, and Sustenance 316'''
'''10.2 产生，依赖和维持 Production, Dependence, and Sustenance 316'''
'''10.3 Causal Beams and Sustenance-Based Causation 318'''
'''10.3 因果束和基于维持的因果关系 Causal Beams and Sustenance-Based Causation 318'''
10.3.1 Causal Beams: Definitions and Implications 318
10.3.1 因果束：定义及其含义 Causal Beams: Definitions and Implications 318
10.3.2 Examples: From Disjunction to General Formulas 320
10.3.2 例子：从析取式到通用公式 Examples: From Disjunction to General Formulas 320
10.3.3 Beams, Preemption, and the Probability of Single-Event Causation 322
10.3.3 束，抢占和单事件因果的概率 Beams, Preemption, and the Probability of Single-Event Causation 322
10.3.4 Path-Switching Causation 324
10.3.4 路径切换因果 Path-Switching Causation 324
10.3.5 Temporal Preemption 325
10.3.5 时序抢占 Temporal Preemption 325
'''10.4 Conclusions 327'''
'''10.4 总结 Conclusions 327'''
=== 11 Reflections, Elaborations, and Discussions with Readers 331 ===
=== 11 跟读者的回应，阐述和讨论 Reflections, Elaborations, and Discussions with Readers 331 ===
'''11.1 Causal, Statistical, and Graphical Vocabulary 331'''
'''11.1 因果，统计和图的相关术语 Causal, Statistical, and Graphical Vocabulary 331'''
11.1.1 Is the Causal-Statistical Dichotomy Necessary? 331
11.1.1 有必要区分因果和统计吗？ Is the Causal-Statistical Dichotomy Necessary? 331
11.1.2 d-Separation without Tears (Chapter 1, pp. 16–18) 335
11.1.2 不痛哭的d-分离（第一章） d-Separation without Tears (Chapter 1, pp. 16–18) 335
'''11.2 Reversing Statistical Time (Chapter 2, p. 58–59) 337'''
'''11.2 逆统计时间（第二章） Reversing Statistical Time (Chapter 2, p. 58–59) 337'''
'''11.3 Estimating Causal Effects 338'''
'''11.3 估计因果效应 Estimating Causal Effects 338'''
11.3.1 The Intuition behind the Back-Door Criterion (Chapter 3, p. 79) 338
11.3.1 后门准则的直观理解（第三章） The Intuition behind the Back-Door Criterion (Chapter 3, p. 79) 338
11.3.2 Demystifying “Strong Ignorability” 341
11.3.2 揭秘“强可忽略性” Demystifying “Strong Ignorability” 341
11.3.3 Alternative Proof of the Back-Door Criterion 344
11.3.3 后门准则的另一种证明 Alternative Proof of the Back-Door Criterion 344
11.3.4 Data vs. Knowledge in Covariate Selection 346
11.3.4 协变量选择中的数据与知识 Data vs. Knowledge in Covariate Selection 346
11.3.5 Understanding Propensity Scores 348
11.3.5 理解倾向得分 Understanding Propensity Scores 348
11.3.6 The Intuition behind do-Calculus 352
11.3.6 do-算子的直观理解 The Intuition behind do-Calculus 352
11.3.7 The Validity of G-Estimation 352
11.3.7 G-估计的有效性 The Validity of G-Estimation 352
'''11.4 Policy Evaluation and the do-Operator 354'''
'''11.4 策略评估和do-算子 Policy Evaluation and the do-Operator 354'''
11.4.1 Identifying Conditional Plans (Section 4.2, p. 113) 354
11.4.1 识别条件计划（4.2节）Identifying Conditional Plans (Section 4.2, p. 113) 354
11.4.2 The Meaning of Indirect Effects 355
11.4.2 间接效应的含义 The Meaning of Indirect Effects 355
11.4.3 Can do(x) Represent Practical Experiments? 358
11.4.3 do(x)能代表实际的实验吗？ Can do(x) Represent Practical Experiments? 358
11.4.4 Is the do(x) Operator Universal? 359
11.4.4 do(x)算子是通用的吗？ Is the do(x) Operator Universal? 359
11.4.5 Causation without Manipulation!!! 361
11.4.5 Causation without Manipulation!!! 361