Statistical inference for direction of dependence in linear models

Détails

ID Serval
serval:BIB_5E7FC67D0CEF
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Institution
Titre
Statistical inference for direction of dependence in linear models
Titre du livre
Statistics and Causality : Methods for Applied Empirical Research
Auteur⸱e⸱s
Dodge Y., Rousson V.
Editeur
Wiley
Lieu d'édition
New York
ISBN
978-1-118-94704-3
Statut éditorial
Publié
Date de publication
2016
Numéro de chapitre
3
Pages
45-62
Langue
anglais
Résumé
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses.
The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology.
Création de la notice
17/06/2016 11:22
Dernière modification de la notice
20/08/2019 15:16
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