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.

Table of Content

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

About the author

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.