Causality : statistical perspectives and applications

著者

• Berzuini, Carlo
• Dawid, Philip
• Bernardinelli, Luisa

書誌事項

Causality : statistical perspectives and applications

edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli

（Wiley series in probability and mathematical statistics）

Wiley, 2012

注記

Includes bibliographical references and index

内容説明・目次

内容説明

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.

目次

List of contributors xv An overview of statistical causality xvii Carlo Berzuini, Philip Dawid and Luisa Bernardinelli 1 Statistical causality: Some historical remarks 1 D.R. Cox 1.1 Introduction 1 1.2 Key issues 2 1.3 Rothamsted view 2 1.4 An earlier controversy and its implications 3 1.5 Three versions of causality 4 1.6 Conclusion 4 References 4 2 The language of potential outcomes 6 Arvid Sjoelander 2.1 Introduction 6 2.2 Definition of causal effects through potential outcomes 7 2.2.1 Subject-specific causal effects 7 2.2.2 Population causal effects 8 2.2.3 Association versus causation 9 2.3 Identification of population causal effects 9 2.3.1 Randomized experiments 9 2.3.2 Observational studies 11 2.4 Discussion 11 References 13 3 Structural equations, graphs and interventions 15 Ilya Shpitser 3.1 Introduction 15 3.2 Structural equations, graphs, and interventions 16 3.2.1 Graph terminology 16 3.2.2 Markovian models 17 3.2.3 Latent projections and semi-Markovian models 19 3.2.4 Interventions in semi-Markovian models 19 3.2.5 Counterfactual distributions in NPSEMs 20 3.2.6 Causal diagrams and counterfactual independence 22 3.2.7 Relation to potential outcomes 22 References 23 4 The decision-theoretic approach to causal inference 25 Philip Dawid 4.1 Introduction 25 4.2 Decision theory and causality 26 4.2.1 A simple decision problem 26 4.2.2 Causal inference 27 4.3 No confounding 28 4.4 Confounding 29 4.4.1 Unconfounding 29 4.4.2 Nonconfounding 30 4.4.3 Back-door formula 31 4.5 Propensity analysis 33 4.6 Instrumental variable 34 4.6.1 Linear model 36 4.6.2 Binary variables 36 4.7 Effect of treatment of the treated 37 4.8 Connections and contrasts 37 4.8.1 Potential responses 37 4.8.2 Causal graphs 39 4.9 Postscript 40 Acknowledgements 40 References 40 5 Causal inference as a prediction problem: Assumptions, identification and evidence synthesis 43 Sander Greenland 5.1 Introduction 43 5.2 A brief commentary on developments since 1970 44 5.2.1 Potential outcomes and missing data 45 5.2.2 The prognostic view 45 5.3 Ambiguities of observational extensions 46 5.4 Causal diagrams and structural equations 47 5.5 Compelling versus plausible assumptions, models and inferences 47 5.6 Nonidentification and the curse of dimensionality 50 5.7 Identification in practice 51 5.8 Identification and bounded rationality 53 5.9 Conclusion 54 Acknowledgments 55 References 55 6 Graph-based criteria of identifiability of causal questions 59 Ilya Shpitser 6.1 Introduction 59 6.2 Interventions from observations 59 6.3 The back-door criterion, conditional ignorability, and covariate adjustment 61 6.4 The front-door criterion 63 6.5 Do-calculus 64 6.6 General identification 65 6.7 Dormant independences and post-truncation constraints 68 References 69 7 Causal inference from observational data: A Bayesian predictive approach 71 Elja Arjas 7.1 Background 71 7.2 A model prototype 72 7.3 Extension to sequential regimes 76 7.4 Providing a causal interpretation: Predictive inference from data 80 7.5 Discussion 82 Acknowledgement 83 References 83 8 Assessing dynamic treatment strategies 85 Carlo Berzuini, Philip Dawid, and Vanessa Didelez 8.1 Introduction 85 8.2 Motivating example 86 8.3 Descriptive versus causal inference 87 8.4 Notation and problem definition 88 8.5 HIV example continued 89 8.6 Latent variables 89 8.7 Conditions for sequential plan identifiability 90 8.7.1 Stability 90 8.7.2 Positivity 91 8.8 Graphical representations of dynamic plans 92 8.9 Abdominal aortic aneurysm surveillance 94 8.10 Statistical inference and computation 95 8.11 Transparent actions 97 8.12 Refinements 98 8.13 Discussion 99 Acknowledgements 99 References 99 9 Causal effects and natural laws: Towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with application to the effects of race and sex 101 Tyler J. VanderWeele and Miguel A. Hernan 9.1 Introduction 101 9.2 Laws of nature and contrary to fact statements 102 9.3 Association and causation in the social and biomedical sciences 103 9.4 Manipulation and counterfactuals 103 9.5 Natural laws and causal effects 104 9.6 Consequences of randomization 107 9.7 On the causal effects of sex and race 108 9.8 Discussion 111 Acknowledgements 112 References 112 10 Cross-classifications by joint potential outcomes 114 Arvid Sjoelander 10.1 Introduction 114 10.2 Bounds for the causal treatment effect in randomized trials with imperfect compliance 115 10.3 Identifying the complier causal effect in randomized trials with imperfect compliance 119 10.4 Defining the appropriate causal effect in studies suffering from truncation by death 121 10.5 Discussion 123 References 124 11 Estimation of direct and indirect effects 126 Stijn Vansteelandt 11.1 Introduction 126 11.2 Identification of the direct and indirect effect 127 11.2.1 Definitions 127 11.2.2 Identification 129 11.3 Estimation of controlled direct effects 132 11.3.1 G-computation 132 11.3.2 Inverse probability of treatment weighting 133 11.3.3 G-estimation for additive and multiplicative models 137 11.3.4 G-estimation for logistic models 141 11.3.5 Case-control studies 142 11.3.6 G-estimation for additive hazard models 143 11.4 Estimation of natural direct and indirect effects 146 11.5 Discussion 147 Acknowledgements 147 References 148 12 The mediation formula: A guide to the assessment of causal pathways in nonlinear models 151 Judea Pearl 12.1 Mediation: Direct and indirect effects 151 12.1.1 Direct versus total effects 151 12.1.2 Controlled direct effects 152 12.1.3 Natural direct effects 154 12.1.4 Indirect effects 156 12.1.5 Effect decomposition 157 12.2 The mediation formula: A simple solution to a thorny problem 157 12.2.1 Mediation in nonparametric models 157 12.2.2 Mediation effects in linear, logistic, and probit models 159 12.2.3 Special cases of mediation models 164 12.2.4 Numerical example 169 12.3 Relation to other methods 170 12.3.1 Methods based on differences and products 170 12.3.2 Relation to the principal-strata direct effect 171 12.4 Conclusions 173 Acknowledgments 174 References 175 13 The sufficient cause framework in statistics, philosophy and the biomedical and social sciences 180 Tyler J. VanderWeele 13.1 Introduction 180 13.2 The sufficient cause framework in philosophy 181 13.3 The sufficient cause framework in epidemiology and biomedicine 181 13.4 The sufficient cause framework in statistics 185 13.5 The sufficient cause framework in the social sciences 185 13.6 Other notions of sufficiency and necessity in causal inference 187 13.7 Conclusion 188 Acknowledgements 189 References 189 14 Analysis of interaction for identifying causal mechanisms 192 Carlo Berzuini, Philip Dawid, Hu Zhang and Miles Parkes 14.1 Introduction 192 14.2 What is a mechanism? 193 14.3 Statistical versus mechanistic interaction 193 14.4 Illustrative example 194 14.5 Mechanistic interaction defined 196 14.6 Epistasis 197 14.7 Excess risk and superadditivity 197 14.8 Conditions under which excess risk and superadditivity indicate the presence of mechanistic interaction 200 14.9 Collapsibility 201 14.10 Back to the illustrative study 202 14.11 Alternative approaches 204 14.12 Discussion 204 Ethics statement 205 Financial disclosure 205 References 206 15 Ion channels as a possible mechanism of neurodegeneration in multiple sclerosis 208 Luisa Bernardinelli, Carlo Berzuini, Luisa Foco, and Roberta Pastorino 15.1 Introduction 208 15.2 Background 209 15.3 The scientific hypothesis 209 15.4 Data 210 15.5 A simple preliminary analysis 211 15.6 Testing for qualitative interaction 213 15.7 Discussion 214 Acknowledgments 216 References 216 16 Supplementary variables for causal estimation 218 Roland R. Ramsahai 16.1 Introduction 218 16.2 Multiple expressions for causal effect 220 16.3 Asymptotic variance of causal estimators 222 16.4 Comparison of causal estimators 222 16.4.1 Supplement C with L or not 223 16.4.2 Supplement L with C or not 224 16.4.3 Replace C with L or not 225 16.5 Discussion 226 Acknowledgements 226 Appendices 227 16.A Estimator given all X's recorded 227 16.B Derivations of asymptotic variances 227 16.C Expressions with correlation coefficients 229 16.D Derivation of I's 230 16.E Relation between 2 rl|t and 2 rl|c 231 References 232 17 Time-varying confounding: Some practical considerations in a likelihood framework 234 Rhian Daniel, Bianca De Stavola and Simon Cousens 17.1 Introduction 234 17.2 General setting 235 17.2.1 Notation 235 17.2.2 Observed data structure 235 17.2.3 Intervention strategies 236 17.2.4 Potential outcomes 237 17.2.5 Time-to-event outcomes 237 17.2.6 Causal estimands 238 17.3 Identifying assumptions 238 17.4 G-computation formula 239 17.4.1 The formula 239 17.4.2 Plug-in regression estimation 240 17.5 Implementation by Monte Carlo simulation 242 17.5.1 Simulating an end-of-study outcome 242 17.5.2 Simulating a time-to-event outcome 242 17.5.3 Inference 242 17.5.4 Losses to follow-up 243 17.5.5 Software 243 17.6 Analyses of simulated data 243 17.6.1 The data 243 17.6.2 Regimes to be compared 244 17.6.3 Parametric modelling choices 245 17.6.4 Results 246 17.7 Further considerations 249 17.7.1 Parametric model misspecification 249 17.7.2 Competing events 249 17.7.3 Unbalanced measurement times 250 17.8 Summary 251 References 251 18 'Natural experiments' as a means of testing causal inferences 253 Michael Rutter 18.1 Introduction 253 18.2 Noncausal interpretations of an association 253 18.3 Dealing with confounders 255 18.4 'Natural experiments' 256 18.4.1 Genetically sensitive designs 257 18.4.2 Children of twins (CoT) design 259 18.4.3 Strategies to identify the key environmental risk feature 261 18.4.4 Designs for dealing with selection bias 263 18.4.5 Instrumental variables to rule out reverse causation 264 18.4.6 Regression discontinuity (RD) designs to deal with unmeasured confounders 265 18.5 Overall conclusion on 'natural experiments' 266 18.5.1 Supported causes 266 18.5.2 Disconfirmed causes 267 Acknowledgement 267 References 268 19 Nonreactive and purely reactive doses in observational studies 273 Paul R. Rosenbaum 19.1 Introduction: Background, example 273 19.1.1 Does a dose-response relationship provide information that distinguishes treatment effects from biases due to unmeasured covariates? 273 19.1.2 Is more chemotherapy for ovarian cancer more effective or more toxic? 274 19.2 Various concepts of dose 277 19.2.1 Some notation: Covariates, outcomes, and treatment assignment in matched pairs 277 19.2.2 Reactive and nonreactive doses of treatment 278 19.2.3 Three test statistics that use doses in different ways 279 19.2.4 Randomization inference in randomized experiments 280 19.2.5 Sensitivity analysis 281 19.2.6 Sensitivity analysis in the example 283 19.3 Design sensitivity 284 19.3.1 What is design sensitivity? 284 19.3.2 Comparison of design sensitivity with purely reactive doses 286 19.4 Summary 287 References 287 20 Evaluation of potential mediators in randomised trials of complex interventions (psychotherapies) 290 Richard Emsley and Graham Dunn 20.1 Introduction 290 20.2 Potential mediators in psychological treatment trials 291 20.3 Methods for mediation in psychological treatment trials 293 20.4 Causal mediation analysis using instrumental variables estimation 297 20.5 Causal mediation analysis using principal stratification 301 20.6 Our motivating example: The SoCRATES trial 302 20.6.1 What are the joint effects of sessions attended and therapeutic alliance on the PANSS score at 18 months? 303 20.6.2 What is the direct effect of random allocation on the PANSS score at 18 months and how is this influenced by the therapeutic alliance? 304 20.6.3 Is the direct effect of the number of sessions attended on the PANSS score at 18 months influenced by therapeutic alliance? 305 20.7 Conclusions 305 Acknowledgements 306 References 307 21 Causal inference in clinical trials 310 Krista Fischer and Ian R. White 21.1 Introduction 310 21.2 Causal effect of treatment in randomized trials 312 21.2.1 Observed data and notation 312 21.2.2 Defining the effects of interest via potential outcomes 312 21.2.3 Adherence-adjusted ITT analysis 314 21.3 Estimation for a linear structural mean model 316 21.3.1 A general estimation procedure 316 21.3.2 Identifiability and closed-form estimation of the parameters in a linear SMM 317 21.3.3 Analysis of the EPHT trial 319 21.4 Alternative approaches for causal inference in randomized trials comparing experimental treatment with a control 321 21.4.1 Principal stratification 321 21.4.2 SMM for the average treatment effect on the treated (ATT) 322 21.5 Discussion 324 References 325 22 Causal inference in time series analysis 327 Michael Eichler 22.1 Introduction 327 22.2 Causality for time series 328 22.2.1 Intervention causality 328 22.2.2 Structural causality 331 22.2.3 Granger causality 332 22.2.4 Sims causality 334 22.3 Graphical representations for time series 335 22.3.1 Conditional distributions and chain graphs 336 22.3.2 Path diagrams and Granger causality graphs 337 22.3.3 Markov properties for Granger causality graphs 338 22.4 Representation of systems with latent variables 339 22.4.1 Marginalization 341 22.4.2 Ancestral graphs 342 22.5 Identification of causal effects 343 22.6 Learning causal structures 346 22.7 A new parametric model 349 22.8 Concluding remarks 351 References 352 23 Dynamic molecular networks and mechanisms in the biosciences: A statistical framework 355 Clive G. Bowsher 23.1 Introduction 355 23.2 SKMs and biochemical reaction networks 356 23.3 Local independence properties of SKMs 358 23.3.1 Local independence and kinetic independence graphs 358 23.3.2 Local independence and causal influence 361 23.4 Modularisation of SKMs 362 23.4.1 Modularisations and dynamic independence 362 23.4.2 MIDIA Algorithm 363 23.5 Illustrative example - MAPK cell signalling 365 23.6 Conclusion 369 23.7 Appendix: SKM regularity conditions 369 Acknowledgements 370 References 370 Index 371

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詳細情報

• NII書誌ID(NCID): BB09775792
• ISBN:
  • 9780470665565
• LCCN: 2011049795
• 出版国コード: uk
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Chichester
• ページ数/冊数: xxv, 377 p.
• 大きさ: 25 cm
• 分類:
  • DC23 : 519.544
• 件名:
  • LCSH : Estimation theory
  • LCSH : Causation
  • LCSH : Causality (Physics)
• 親書誌ID:
  • BA00019013