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小第493行：
第493行： − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − +
翻译完了所有目录信息
11.4.4 do(x)算子是通用的吗？ 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 没有操作的因果关系！ Causation without Manipulation!!! 361
11.4.6 Hunting Causes with Cartwright 362
11.4.6 与卡特赖特一起追寻原因 Hunting Causes with Cartwright 362
11.4.7 The Illusion of Nonmodularity 364
11.4.7 非模块化的错觉 The Illusion of Nonmodularity 364
'''11.5 Causal Analysis in Linear Structural Models 366'''
'''11.5 线性结构模型中的因果分析 Causal Analysis in Linear Structural Models 366'''
11.5.1 General Criterion for Parameter Identification (Chapter 5, pp. 149–54) 366
11.5.1 参数识别的通用准则（第五章） General Criterion for Parameter Identification (Chapter 5, pp. 149–54) 366
11.5.2 The Causal Interpretation of Structural Coefficients 366
11.5.2 结构系数的因果解释 The Causal Interpretation of Structural Coefficients 366
11.5.3 Defending the Causal Interpretation of SEM (or, SEM Survival Kit) 368
11.5.3 为SEM（或者SEM急救包）的因果解释辩护 Defending the Causal Interpretation of SEM (or, SEM Survival Kit) 368
11.5.4 Where Is Economic Modeling Today? – Courting Causes with Heckman 374
11.5.4 今天的经济模型在哪？-与赫克曼一起追寻原因 Where Is Economic Modeling Today? – Courting Causes with Heckman 374
11.5.5 External Variation versus Surgery 376
11.5.5 外部变化与外科手术 External Variation versus Surgery 376
'''11.6 Decisions and Confounding (Chapter 6) 380'''
'''11.6 决策与混杂（第六章） Decisions and Confounding (Chapter 6) 380'''
11.6.1 Simpson's Paradox and Decision Trees 380
11.6.1 辛普森悖论和决策树 Simpson's Paradox and Decision Trees 380
11.6.2 Is Chronological Information Sufficient for Decision Trees? 382
11.6.2 时序信息对决策树来说是否充分？ Is Chronological Information Sufficient for Decision Trees? 382
11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384
11.6.3 林德利关于因果性，决策树和贝叶斯主义的理解 Lindley on Causality, Decision Trees, and Bayesianism 384
11.6.4 Why Isn't Confounding a Statistical Concept? 387
11.6.4 为什么混杂不是统计学的概念？ Why Isn't Confounding a Statistical Concept? 387
'''11.7 The Calculus of Counterfactuals 389'''
'''11.7 计算反事实 The Calculus of Counterfactuals 389'''
11.7.1 Counterfactuals in Linear Systems 389
11.7.1 线性系统中的反事实 Counterfactuals in Linear Systems 389
11.7.2 The Meaning of Counterfactuals 391
11.7.2 反事实的意义 The Meaning of Counterfactuals 391
11.7.3 d-Separation of Counterfactuals 393
11.7.3 反事实中的d-分离 d-Separation of Counterfactuals 393
'''11.8 Instrumental Variables and Noncompliance 395'''
'''11.8 工具变量和不依从性 Instrumental Variables and Noncompliance 395'''
11.8.1 Tight Bounds under Noncompliance 395
11.8.1 不依从性下的紧边界 Tight Bounds under Noncompliance 395
'''11.9 More on Probabilities of Causation 396'''
'''11.9 更多关于因果的概率 More on Probabilities of Causation 396'''
11.9.1 Is "Guilty with Probability One" Ever Possible? 396
11.9.1 “有罪的概率为1”还有可能吗？ Is "Guilty with Probability One" Ever Possible? 396
11.9.2 Tightening the Bounds on Probabilities of Causation 398
11.9.2 收紧因果概率的边界 Tightening the Bounds on Probabilities of Causation 398