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.
Tabella dei contenuti
Preface (editors)
1 Statistical Causality: Some Historical Remarks
AUTHOR(S): D.R. Cox
2 The Language of Potential Outcomes
AUTHOR(S): Arvid Sjolander
3 Structural Equations, Graphs and Interventions
AUTHOR(S): Ilya Shpitser
4 The Decision-Theoretic Approach to Causal Inference
AUTHOR(S): A. Philip Dawid
5 Causal Inference as a Prediction Problem: Assumptions, Identification, and Evidence Synthesis
AUTHOR(S): Sander Greenland
6 Graph-Based Criteria of Identifiability of Causal Questions
AUTHOR(S): Ilya Shpitser
7 Causal inference from observational data: a Bayesian predictive approach
AUTHOR(S): Elja Arjas
8 Causal Inference from Observing Sequences of Actions
9 Causal Effects and Natural Laws: towards a Conceptualization of Causal Counterfactuals for Non-Manipulable Exposures, with Application to the Effects of Race and Sex
AUTHOR(S): Tyler J. Vander Weele and Miguel A. Hernan
10 Cross-Classifications by Joint Potential Outcomes
AUTHOR(S): Arvid Sjolander
11 Estimation of Direct and Indirect Effects
AUTHOR(S): Stijn Vansteelandt
12 The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models
AUTHOR(S): Judea Pearl
13 The Sufficient Cause Framework in Statistics, Philosophy and the Biomedical and Social Sciences
AUTHOR(S): Tyler J. Vander Weele
14 Inference about Biological Mechanism on the Basis of Epidemiological Data
AUTHOR(S): Carlo Berzuini and A. Philip Dawid
15 Ion Channels and Multiple Sclerosis
AUTHOR(S): Luisa Bernardinelli, Carlo Berzuini, Luisa Foco and Roberta Pastorino
16 Supplementary Variables For Causal Estimation
AUTHOR(S): Roland R. Ramsahai
17 Time-Varying Confounding: Some Practical Considerations in a Likelihood Framework
AUTHOR(S): Rhian Daniel, Bianca De Stavola and Simon Cousens
18 Natural Experiments as a Means of Testing Causal Inferences
AUTHOR(S): Michael Rutter
19 Nonreactive and Purely Reactive Doses in Observational Studies
AUTHOR(S): Paul R. Rosenbaum
20 Evaluation of Potential Mediators in Randomized Trials of Complex Interventions(Psychotherapies)
AUTHOR(S): Richard Emsley and Graham Dunn
21 Causal Inference in Clinical Trials
AUTHOR(S): Krista Fischer and Ian R. White
22 Granger Causality and Causal Inference in Time Series Analysis
AUTHOR(S): Michael Eichler
23 Dynamic Molecular Networks and Mechanisms in the Biosciences: A Statistical
Framework
AUTHOR(S): Clive G. Bowsher
Index
Circa l’autore
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.
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