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.

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Yes, you can access Causality by Carlo Berzuini, Philip Dawid, Luisa Bernardinell, Carlo Berzuini,Philip Dawid,Luisa Bernardinell in PDF and/or ePUB format, as well as other popular books in Matematica & Probabilità e statistica. We have over one million books available in our catalogue for you to explore.

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Year

Print ISBN

eBook ISBN

Edition

Topic

Matematica

Subtopic

Probabilità e statistica

Cover
Series
Title Page
Copyright
List of contributors
An overview of statistical causality
Chapter 1: Statistical causality: Some historical remarks
Chapter 2: The language of potential outcomes
Chapter 3: Structural equations, graphs and interventions
Chapter 4: The decision-theoretic approach to causal inference
Chapter 5: Causal inference as a prediction problem: Assumptions, identification and evidence synthesis
Chapter 6: Graph-based criteria of identifiability of causal questions
Chapter 7: Causal inference from observational data: A Bayesian predictive approach
Chapter 8: Assessing dynamic treatment strategies
Chapter 9: Causal effects and natural laws: Towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with application to the effects of race and sex
Chapter 10: Cross-classifications by joint potential outcomes
Chapter 11: Estimation of direct and indirect effects
Chapter 12: The mediation formula: A guide to the assessment of causal pathways in nonlinear models
Chapter 13: The sufficient cause framework in statistics, philosophy and the biomedical and social sciences
Chapter 14: Analysis of interaction for identifying causal mechanisms
Chapter 15: Ion channels as a possible mechanism of neurodegeneration in multiple sclerosis
Chapter 16: Supplementary variables for causal estimation
Chapter 17: Time-varying confounding: Some practical considerations in a likelihood framework
Chapter 18: ‘Natural experiments’ as a means of testing causal inferences
Chapter 19: Nonreactive and purely reactive doses in observational studies
Chapter 20: Evaluation of potential mediators in randomised trials of complex interventions (psychotherapies)
Chapter 21: Causal inference in clinical trials
Chapter 22: Causal inference in time series analysis
Chapter 23: Dynamic molecular networks and mechanisms in the biosciences: A statistical framework
Index
Series

About this book

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Table of contents