Statistical inference for direction of dependence in linear models

TitreStatistical inference for direction of dependence in linear models
Publication TypeBook Chapter
Year of Publication2016
AuthorsDodge, Y, Rousson, V
Book TitleStatistics and Causality : Methods for Applied Empirical Research
Pagination45-62
PublisherWiley
Other Numbersserval:BIB_5E7FC67D0CEF
Abstract

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.

Citation Key / SERVAL IDRN181

                         

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