Titre
Statistical inference for direction of dependence in linear models
Type
chapitre
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Dodge, Y.
Auteure/Auteur
Rousson, V.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
Maison d’édition
Wiley
Lieu d’édition
New York
Titre du livre ou conférence/colloque
Statistics and Causality : Methods for Applied Empirical Research
ISBN du livre
978-1-118-94704-3
Statut éditorial
Publié
Date de publication
2016
Chapitre
3
Première page
45
Dernière page/numéro d’article
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.
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.
PID Serval
serval:BIB_5E7FC67D0CEF
Date de création
2016-06-17T09:22:33.597Z
Date de création dans IRIS
2025-05-20T18:03:11Z