Verfügbarkeit: Causality | Technische Universität München

Causality: models, reasoning, and inference

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Bibliographische Detailangaben
Beteilige Person:	Pearl, Judea 1936- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Cambridge [u.a.] Cambridge Univ. Press 2009
Ausgabe:	Second edition
Schlagwörter:	Kausalität - Mathematik Kausalität - Methode - Wissenschaft - Kausalitätswahrnehmung Vernunft - Kausalmodell Causation Probabilities Wahrscheinlichkeitsrechnung Wissenschaft Statistische Schlussweise Vernunft Kausalität Kausalmodell Kausalitätswahrnehmung Methode Unsicheres Schließen Statistische Analyse Wahrscheinlichkeit
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018618229&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Hier auch später erschienene, unveränderte Nachdrucke
Umfang:	XIX, 464 Seiten Illustrationen, Diagramme
ISBN:	9780521895606 0521773628

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Datensatz im Suchindex

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adam_text	Contents Preface to the First Edition page χν Preface to the Second Edition xix 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 ¿/-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 Contents 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 65 3.1 Introduction 66 3.2 Intervention in Markovian Models 68 3.2.1 Graphs as Models of Interventions 68 3.2.2 Interventions as Variables 70 3.2.3 Computing the Effect of Interventions 72 3.2.4 Identification of Causal Quantities 77 3.3 Controlling Confounding Bias 78 3.3.1 The Back-Door Criterion 79 3.3.2 The Front-Door Criterion 81 3.3.3 Example: Smoking and the Genotype Theory 83 3.4 A Calculus of Intervention 85 3.4.1 Preliminary Notation 85 3.4.2 Inference Rules 85 3.4.3 Symbolic Derivation of Causal Effects: An Example 86 3.4.4 Causal Inference by Surrogate Experiments 88 3.5 Graphical Tests of Identi fi ability 89 3.5.1 Identifying Models 91 3.5.2 Nonidentifying Models 93 3.6 Discussion 94 3.6.1 Qualifications and Extensions 94 3.6.2 Diagrams as a Mathematical Language 96 3.6.3 Translation from Graphs to Potential Outcomes 98 3.6.4 Relations to Robins s G-Estimation 102 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 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 132 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 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 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 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 Imperfect Experiments: Bounding Effects and Counterfactuals 259 8.1 Introduction 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: The Effect of Radiation on 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 ¿-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 do-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 Heekman 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 J-Separation of Counterfactuals 393 Contents xiii 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 429 Name Index 453 Subject Index 459
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author	Pearl, Judea 1936-
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id	DE-604.BV035758307
illustrated	Illustrated
indexdate	2024-12-20T13:59:30Z
institution	BVB
isbn	9780521895606 0521773628
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-018618229
oclc_num	460284744
open_access_boolean
owner	DE-83 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-12 DE-91G DE-BY-TUM DE-11
owner_facet	DE-83 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-12 DE-91G DE-BY-TUM DE-11
physical	XIX, 464 Seiten Illustrationen, Diagramme
publishDate	2009
publishDateSearch	2009
publishDateSort	2009
publisher	Cambridge Univ. Press
record_format	marc
spellingShingle	Pearl, Judea 1936- Causality models, reasoning, and inference Kausalität - Mathematik Kausalität - Methode - Wissenschaft - Kausalitätswahrnehmung Vernunft - Kausalmodell Causation Probabilities Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Wissenschaft (DE-588)4066562-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Vernunft (DE-588)4063106-0 gnd Kausalität (DE-588)4030102-3 gnd Kausalmodell (DE-588)4123496-0 gnd Kausalitätswahrnehmung (DE-588)4290135-2 gnd Methode (DE-588)4038971-6 gnd Unsicheres Schließen (DE-588)4361044-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd
subject_GND	(DE-588)4064324-4 (DE-588)4066562-8 (DE-588)4182963-3 (DE-588)4063106-0 (DE-588)4030102-3 (DE-588)4123496-0 (DE-588)4290135-2 (DE-588)4038971-6 (DE-588)4361044-4 (DE-588)4116599-8 (DE-588)4137007-7
title	Causality models, reasoning, and inference
title_auth	Causality models, reasoning, and inference
title_exact_search	Causality models, reasoning, and inference
title_full	Causality models, reasoning, and inference Judea Pearl
title_fullStr	Causality models, reasoning, and inference Judea Pearl
title_full_unstemmed	Causality models, reasoning, and inference Judea Pearl
title_short	Causality
title_sort	causality models reasoning and inference
title_sub	models, reasoning, and inference
topic	Kausalität - Mathematik Kausalität - Methode - Wissenschaft - Kausalitätswahrnehmung Vernunft - Kausalmodell Causation Probabilities Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Wissenschaft (DE-588)4066562-8 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Vernunft (DE-588)4063106-0 gnd Kausalität (DE-588)4030102-3 gnd Kausalmodell (DE-588)4123496-0 gnd Kausalitätswahrnehmung (DE-588)4290135-2 gnd Methode (DE-588)4038971-6 gnd Unsicheres Schließen (DE-588)4361044-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd
topic_facet	Kausalität - Mathematik Kausalität - Methode - Wissenschaft - Kausalitätswahrnehmung Vernunft - Kausalmodell Causation Probabilities Wahrscheinlichkeitsrechnung Wissenschaft Statistische Schlussweise Vernunft Kausalität Kausalmodell Kausalitätswahrnehmung Methode Unsicheres Schließen Statistische Analyse Wahrscheinlichkeit
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018618229&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT pearljudea causalitymodelsreasoningandinference

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