Causality (eBook, PDF)

Statistical Perspectives and Applications
Redaktion: Berzuini, Carlo; Bernardinell, Luisa; Dawid, Philip

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 640
• Erscheinungstermin: 25. Mai 2012
• Englisch
• ISBN-13: 9781119945703
• Artikelnr.: 37356288

Autorenporträt

Carlo Berzuini and Philip Dawid, Statistical Labority, centre for Mathematical Sciences, University of Cambridge, UK. Luisa Bernardinelli, MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.

Inhaltsangabe

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 Sjölander 2.1 Introduction 6 2.2 Definition
of causal effects through potential outcomes 7 2.3 Identification of
population causal effects 9 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 References 23 4 The
decision-theoretic approach to causal inference 25 Philip Dawid 4.1
Introduction 25 4.2 Decision theory and causality 26 4.3 No confounding 28
4.4 Confounding 29 4.5 Propensity analysis 33 4.6 Instrumental variable 34
4.7 Effect of treatment of the treated 37 4.8 Connections and contrasts 37
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 5.3 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 8.8
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. Hernán 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 Sjölander 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 11.3 Estimation of controlled direct effects 132 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 12.2 The mediation formula: A
simple solution to a thorny problem 157 12.3 Relation to other methods 170
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 16.5 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 rho2 rl t
and rho2 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
17.3 Identifying assumptions 238 17.4 G-computation formula 239 17.5
Implementation by Monte Carlo simulation 242 17.6 Analyses of simulated
data 243 17.7 Further considerations 249 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 18.5 Overall conclusion on 'natural experiments' 266
Acknowledgement 267 References 268 19 Nonreactive and purely reactive doses
in observational studies 273 Paul R. Rosenbaum 19.1 Introduction:
Background, example 273 19.2 Various concepts of dose 277 19.3 Design
sensitivity 284 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 20.7 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 21.3 Estimation for a linear structural mean model 316 21.4 Alternative
approaches for causal inference in randomized trials comparing experimental
treatment with a control 321 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 22.3 Graphical representations for time
series 335 22.4 Representation of systems with latent variables 339 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 23.4 Modularisation of SKMs 362 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