Buch, Englisch, 416 Seiten, Format (B × H): 177 mm x 261 mm, Gewicht: 772 g
Statistical Perspectives and Applications
Buch, Englisch, 416 Seiten, Format (B × H): 177 mm x 261 mm, Gewicht: 772 g
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-0-470-66556-5
Verlag: Wiley
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.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
Weitere Infos & Material
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
2.2.1 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. 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
11.2.1 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
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