Carlo Berzuini & Philip Dawid 
Causality 
Statistical Perspectives and Applications

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.

Tabela de Conteúdo

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

Sobre o 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.