Berzuini / Dawid / Bernardinell - Causality

Causality

Statistical Perspectives and Applications

Medium: Buch

ISBN: 978-0-470-66556-5

Verlag: Wiley

Erscheinungstermin: 13.08.2012

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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.

Produkteigenschaften

Artikelnummer: 9780470665565
Medium: Buch
ISBN: 978-0-470-66556-5
Verlag: Wiley
Erscheinungstermin: 13.08.2012
Sprache(n): Englisch
Auflage: 1. Auflage 2012
Serie: Wiley Series in Probability and Statistics
Produktform: Gebunden
Gewicht: 772 g
Seiten: 416
Format (B x H x T): 177 x 261 x 25 mm
Ausgabetyp: Kein, Unbekannt

Themen

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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