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0 (hodnocen0 x )

BK

2nd ed.

Cambridge : Cambridge University Press, 2009

xix, 464 s. : il., portréty, faksimile ; 27 cm

ISBN 978-0-521-89560-6 (váz.)

Popsáno dle dotisku vydaného v roce 2010

Obsahuje bibliografii na s. 429-452, bibliografické odkazy a rejstřík

000236271

Contents // I // Preface to the First Edition // Preface to the Second Edition // 1 Introduction to Probabilities, Graphs, and Causal Models // 1.1 Introduction to Probability Theory // 1.1.1 Why Probabilities? // 1.1.2 Basic Concepts in Probability Theory // 1.1.3 Combining Predictive and Diagnostic Supports // 1.1.4 Random Variables and Expectations // 1.1.5 Conditional Independence and Graphoids // 1.2 Graphs and Probabilities // 1.2.1 Graphical Notation and Terminology // 1.2.2 Bayesian Networks // 1.2.3 The d-Separation Criterion // 1.2.4 Inference with Bayesian Networks // 1.3 Causal Bayesian Networks // 1.3.1 Causal Networks as Oracles for Interventions // 1.3.2 Causal Relationships and Their Stability // 1.4 Functional Causal Models // 1.4.1 Structural Equations // 1.4.2 Probabilistic Predictions in Causal Models // 1.4.3 Interventions and Causal Effects in Functional Models // 1.4.4 Counterfactuals in Functional Models // 1.5 Causal versus Statistical Terminology // 2 A Theory of Inferred Causation // 2.1 Introduction - The Basic Intuitions // 2.2 The Causal Discovery Framework // 2.3 Model Preference (Occam’s Razor) // 2.4 Stable Distributions // 2.5 Recovering DAG Structures // 2.6 Recovering Latent Structures // page xv // xix // 1 // 1 // 1 // 2 // 6 // 8 // 11 // 12 // 12 // 13 // 16 // 20 // 21 // 22 // 24 // 26 // 27 // 30 // 32 // 33 // 38 // 41 // 42 // 43 // 45 // 48 // 49 // 51 // • • // Vil // Contents // • • • // Vlll

// 2.7 Local Criteria for Inferring Causal Relations 54 // 2.8 Nontemporal Causation and Statistical Time 57 // 2.9 Conclusions 59 // 2.9.1 On Minimality, Markov, and Stability 61 // Causal Diagrams and the Identification of Causal Effects // 3.1 Introduction // 3.2 Intervention in Markovian Models // 3.2.1 Graphs as Models of Interventions // 3.2.2 Interventions as Variables // 3.2.3 Computing the Effect of Interventions // 3.2.4 Identification of Causal Quantities // 3.3 Controlling Confounding Bias // 3.3.1 The Back-Door Criterion // 3.3.2 The Front-Door Criterion // 3.3.3 Example: Smoking and the Genotype Theory // 3.4 A Calculus of Intervention // 3.4.1 Preliminary Notation // 3.4.2 Inference Rules // 3.4.3 Symbolic Derivation of Causal Effects: An Example // 3.4.4 Causal Inference by Surrogate Experiments // 3.5 Graphical Tests of Identifiability // 3.5.1 Identifying Models // 3.5.2 Nonidentifying Models // 3.6 Discussion // 3.6.1 Qualifications and Extensions // 3.6.2 Diagrams as a Mathematical Language // 3.6.3 Translation from Graphs to Potential Outcomes // 3.6.4 Relations to Robins’s G-Estimation // 65 // 66 // 68 // 68 // 70 // 72 // 77 // 78 // 79 // 81 // 83 // 85 // 85 // 85 // 86 // 88 // 89 // 91 // 93 // 94 // 94 // 96 // 98 // 102 // 4 Actions, Plans, and Direct Effects 107 // 4.1 Introduction 108 // 4.1.1 Actions, Acts, and Probabilities 108 // 4.1.2 Actions in Decision Analysis 110 // 4.1.3 Actions and Counterfactuals 112 // 4.2 Conditional Actions

and Stochastic Policies 113 // 4.3 When Is the Effect of an Action Identifiable? 114 // 4.3.1 Graphical Conditions for Identification 114 // 4.3.2 Remarks on Efficiency 116 // 4.3.3 Deriving a Closed-Form Expression // for Control Queries 117 // 4.3.4 Summary 118 // 4.4 The Identification of Dynamic Plans 118 // 4.4.1 Motivation 118 // 4.4.2 Plan Identification: Notation and Assumptions 120 // Contents // ix // 4.4.3 Plan Identification: The Sequential Back-Door Criterion 121 // 4.4.4 Plan Identification: A Procedure 124 // 4.5 Direct and Indirect Effects 126 // 4.5.1 Direct versus Total Effects 126 // 4.5.2 Direct Effects, Definition, and Identification 127 // 4.5.3 Example: Sex Discrimination in College Admission 128 // 4.5.4 Natural Direct Effects 130 // 4.5.5 Indirect Effects and the Mediation Formula 132 // 5 Causality and Structural Models in Social Science and Economics 133 // 5.1 Introduction 134 // 5.1.1 Causality in Search of a Language 134 // 5.1.2 SEM: How Its Meaning Became Obscured 135 // 5.1.3 Graphs as a Mathematical Language 138 // 5.2 Graphs and Model Testing 140 // 5.2.1 The Testable Implications of Structural Models 140 // 5.2.2 Testing the Testable 144 // 5.2.3 Model Equivalence 145 // 5.3 Graphs and Identifiability 149 // 5.3.1 Parameter Identification in Linear Models 149 // 5.3.2 Comparison to Nonparametric Identification 154 // 5.3.3 Causal Effects: The Interventional Interpretation of // Structural Equation Models 157 // 5.4 Some Conceptual Underpinnings

159 // 5.4.1 What Do Structural Parameters Really Mean? 159 // 5.4.2 Interpretation of Effect Decomposition 163 // 5.4.3 Exogeneity, Superexogeneity, and Other Frills 165 // 5.5 Conclusion 170 // 5.6 Postscript for the Second Edition 171 // 5.6.1 An Econometric Awakening? 171 // 5.6.2 Identification in Linear Models 171 // 5.6.3 Robustness of Causal Claims 172 // 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 // 6.1.2 A Tale of Statistical Agony 175 // 6.1.3 Causality versus Exchangeability 177 // 6.1.4 A Paradox Resolved (Or: What Kind of Machine Is Man?) 180 // 6.2 Why There Is No Statistical Test for Confounding, Why Many // Think There Is, and Why They Are Almost Right 182 // 6.2.1 Introduction 182 // 6.2.2 Causal and Associational Definitions 184 // 6.3 How the Associational Criterion Fails 185 // 6.3.1 Failing Sufficiency via Marginality 185 // 6.3.2 Failing Sufficiency via Closed-World Assumptions 186 // X // Contents // 6.3.3 Failing Necessity via Barren Proxies 186 // 6.3.4 Failing Necessity via Incidental Cancellations 188 // 6.4 Stable versus Incidental Unbiasedness 189 // 6.4.1 Motivation 189 // 6.4.2 Formal Definitions 191 // 6.4.3 Operational Test for Stable No-Confounding 192 // 6.5 Confounding, Collapsibility, and Exchangeability 193 // 6.5.1 Confounding and Collapsibility 193 // 6.5.2 Confounding versus Confounders 194 // 6.5.3 Exchangeability versus Structural

Analysis of Confounding 196 // 6.6 Conclusions 199 // 7 The Logic of Structure-Based Counterfactuals 201 // 7.1 Structural Model Semantics 202 // 7.1.1 Definitions: Causal Models, Actions, and Counterfactuals 202 // 7.1.2 Evaluating Counterfactuals: Deterministic Analysis 207 // 7.1.3 Evaluating Counterfactuals: Probabilistic Analysis 212 // 7.1.4 The Twin Network Method 213 // 7.2 Applications and Interpretation of Structural Models 215 // 7.2.1 Policy Analysis in Linear Econometric Models: // An Example 215 // 7.2.2 The Empirical Content of Counterfactuals 217 // 7.2.3 Causal Explanations, Utterances, and Their Interpretation 221 // 7.2.4 From Mechanisms to Actions to Causation 223 // 7.2.5 Simon’s Causal Ordering 226 // 7.3 Axiomatic Characterization 228 // 7.3.1 The Axioms of Structural Counterfactuals 228 // 7.3.2 Causal Effects from Counterfactual Logic: An Example 231 // 7.3.3 Axioms of Causal Relevance 234 // 7.4 Structural and Similarity-Based Counterfactuals 238 // 7.4.1 Relations to Lewis’s Counterfactuals 238 // 7.4.2 Axiomatic Comparison 240 // 7.4.3 Imaging versus Conditioning 242 // 7.4.4 Relations to the Neyman-Rubin Framework 243 // 7.4.5 Exogeneity and Instruments: Counterfactual and // Graphical Definitions 245 // 7.5 Structural versus Probabilistic Causality 249 // 7.5.1 The Reliance on Temporal Ordering 249 // 7.5.2 The Perils of Circularity 250 // 7.5.3 Challenging the Closed-World Assumption, with Children 252 // 7.5.4 Singular versus General

Causes 253 // 7.5.5 Summary 256 // 8 // Imperfect Experiments: Boundin // I // 8.1 Introduction // Effects and Counterfactuals // 259 // 259 // 8.1.1 Imperfect and Indirect Experiments 259 // 8.1.2 Noncompliance and Intent to Treat 261 // Contents // xi // 8.2 Bounding Causal Effects with Instrumental Variables 262 // 8.2.1 Problem Formulation: Constrained Optimization 262 // 8.2.2 Canonical Partitions: The Evolution of // Finite-Response Variables 263 // 8.2.3 Linear Programming Formulation 266 // 8.2.4 The Natural Bounds 268 // 8.2.5 Effect of Treatment on the Treated (ETT) 269 // 8.2.6 Example: The Effect of Cholestyramine 270 // 8.3 Counterfactuals and Legal Responsibility 271 // 8.4 A Test for Instruments 274 // 8.5 A Bayesian Approach to Noncompliance 275 // 8.5.1 Bayesian Methods and Gibbs Sampling 275 // 8.5.2 The Effects of Sample Size and Prior Distribution 277 // 8.5.3 Causal Effects from Clinical Data with Imperfect // Compliance 277 // 8.5.4 Bayesian Estimate of Single-Event Causation 280 // 8.6 Conclusion 281 // 9 Probability of Causation: Interpretation and Identification 283 // 9.1 Introduction 283 // 9.2 Necessary and Sufficient Causes: Conditions of Identification 286 // 9.2.1 Definitions, Notation, and Basic Relationships 286 // 9.2.2 Bounds and Basic Relationships under Exogeneity 289 // 9.2.3 Identifiability under Monotonicity and Exogeneity 291 // 9.2.4 Identifiability under Monotonicity and Nonexogeneity 293 // 9.3 Examples and Applications 296 // 9.3.1

Example 1: Betting against a Fair Coin 296 // 9.3.2 Example 2: The Firing Squad 297 // 9.3.3 Example 3: Was Radiation the Cause of Leukemia? 299 // 9.3.4 Example 4: Legal Responsibility from Experimental and // Nonexperimental Data 302 // 9.3.5 Summary of Results 303 // 9.4 Identification in Nonmonotonic Models 304 // 9.5 Conclusions 307 // 10 The Actual Cause 309 // 10.1 Introduction: The Insufficiency of Necessary Causation 309 // 10.1.1 Singular Causes Revisited 309 // 10.1.2 Preemption and the Role of Structural Information 311 // 10.1.3 Overdetermination and Quasi-Dependence 313 // 10.1.4 Mackie’s INUS Condition 313 // 10.2 Production, Dependence, and Sustenance 316 // 10.3 Causal Beams and Sustenance-Based Causation 318 // 10.3.1 Causal Beams: Definitions and Implications 318 // 10.3.2 Examples: From Disjunction to General Formulas 320 // 10.3.3 Beams, Preemption, and the Probability of // Single-Event Causation 322 // xii Contents // 10.3.4 Path-Switching Causation 324 // 10.3.5 Temporal Preemption 325 // 10.4 Conclusions 327 // 11 Reflections, Elaborations, and Discussions with Readers 331 // 11.1 Causal, Statistical, and Graphical Vocabulary 331 // 11.1.1 Is the Causal-Statistical Dichotomy Necessary? 331 // 11.1.2 d-Separation without Tears (Chapter 1, pp. 16-18) 335 // 11.2 Reversing Statistical Time (Chapter 2, p. 58-59) 337 // 11.3 Estimating Causal Effects 338 // 11.3.1 The Intuition behind the Back-Door Criterion // (Chapter 3, p. 79) 338 // 11.3.2 Demystifying

"Strong Ignorability" 341 // 11.3.3 Alternative Proof of the Back-Door Criterion 344 // 11.3.4 Data vs. Knowledge in Covariate Selection 346 // 11.3.5 Understanding Propensity Scores 348 // 11.3.6 The Intuition behind do-Calculus 352 // 11.3.7 The Validity of G-Estimation 352 // 11.4 Policy Evaluation and the Jo-Operator 354 // 11.4.1 Identifying Conditional Plans (Section 4.2, p. 113) 354 // 11.4.2 The Meaning of Indirect Effects 355 // 11.4.3 Can do(x) Represent Practical Experiments? 358 // 11.4.4 Is the do{x) Operator Universal? 359 // 11.4.5 Causation without Manipulation!!! 361 // 11.4.6 Hunting Causes with Cartwright 362 // 11.4.7 The Illusion of Nonmodularity 364 // 11.5 Causal Analysis in Linear Structural Models 366 // 11.5.1 General Criterion for Parameter Identification // (Chapter 5, pp. 149-54) 366 // 11.5.2 The Causal Interpretation of Structural Coefficients . 366 // 11.5.3 Defending the Causal Interpretation of SEM (or, SEM // Survival Kit) 368 // 11.5.4 Where Is Economic Modeling Today? - Courting // Causes with Heckman 374 // 11.5.5 External Variation versus Surgery 376 // 11.6 Decisions and Confounding (Chapter 6) 380 // 11.6.1 Simpson’s Paradox and Decision Trees 380 // 11.6.2 Is Chronological Information Sufficient for // Decision Trees? 382 // 11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384 // 11.6.4 Why Isn’t Confounding a Statistical Concept? 387 // 11.7 The Calculus of Counterfactuals 389 // 11.7.1 Counterfactuals in Linear

Systems 389 // 11.7.2 The Meaning of Counterfactuals 391 // 11.7.3 d-Separation of Counterfactuals 393 // • • • // Xlll // Contents // 11.8 Instrumental Variables and Noncompliance 395 // 11.8.1 Tight Bounds under Noncompliance 395 // 11.9 More on Probabilities of Causation 396 // 11.9.1 Is "Guilty with Probability One” Ever Possible? 396 // 11.9.2 Tightening the Bounds on Probabilities of Causation 398 // Epilogue // The Art and Science of Cause and Effect // A public lecture delivered in November 1996 as part of // the UCLA Faculty Research Lectureship Program // 401 // Bibliography // Name Index // Subject Index // 429 // 454 // 460