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Informationen zum Autor 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. Klappentext A state of the art volume on statistical causalityCausality: 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. Zusammenfassung Providing a thorough treatment on statistical causality, this resource presents a broad collection of contributions from experts in their fields. Methods and their applications are provided with theoretical background and emphasis is given to practice rather than theory, with technical content kept to a minimum. Inhaltsverzeichnis 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 Sj?ander 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 developme...
