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.
Spis treści
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
O autorze
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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