Causality: Statistical Perspectives and Applications
-23 %

Causality: Statistical Perspectives and Applications

 Buch
Besorgungstitel | Lieferzeit:3-5 Tage I

Unser bisheriger Preis:ORGPRICE: 91,50 €

Jetzt 70,31 €*

Alle Preise inkl. MwSt. | zzgl. Versand
ISBN-13:
9780470665565
Einband:
Buch
Erscheinungsdatum:
13.08.2012
Seiten:
377
Autor:
Carlo Berzuini
Gewicht:
771 g
Format:
249x175x25 mm
Serie:
986, Wiley Series in Probability an
Sprache:
Englisch
Beschreibung:

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
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 comp
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.