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Causal Inference in Econometrics / edited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta

Contributor(s): Huynh, Van-Nam, editor literario | Kreinovich, Vladik, editor literario | Sriboonchitta, Songsak., editor literario | SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Studies in Computational Intelligence, 1860-949X; 622).Publisher: Cham : Springer International Publishing, 2016Edition: 1st ed.Description: 1 recurso en línea (XI, 638 páginas) : 106 ilustraciones, 15 ilustraciones en color.ISBN: 9783319272849.Subject: Economía matemáticaDDC classification: 006.3 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources Abstract: This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
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Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias Sociales HB139 .C387 2016 EB (Browse shelf(Opens below)) .i11589668 Acceso electrónico eBOOK .i11589668
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This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

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