Causal Inference in Econometrics [electronic resource] / edited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta.
Contributor(s): Huynh, Van-Nam [editor.]
| Kreinovich, Vladik [editor.] | Sriboonchitta, Songsak [editor.] | SpringerLink (Online service).
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BookSeries: Studies in Computational Intelligence: 622Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Edition: 1st ed. 2016.Description: XI, 638 p. 106 illus., 91 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319272849.Subject(s): Computational intelligence | Social sciences—Mathematics | Security systems | Computational Intelligence | Mathematics in Business, Economics and Finance | Security Science and TechnologyAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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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