Overview
- theoretical foundations and applications
- Written by experts in the field
- Presents recent research
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 622)
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Table of contents (40 chapters)
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Fundamental Theory
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Applications
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Causal Inference in Econometrics
Editors: Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-27284-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-27283-2Published: 06 January 2016
Softcover ISBN: 978-3-319-80108-7Published: 30 March 2018
eBook ISBN: 978-3-319-27284-9Published: 28 December 2015
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XI, 638
Number of Illustrations: 91 b/w illustrations, 15 illustrations in colour
Topics: Computational Intelligence, Quantitative Finance, Quality Control, Reliability, Safety and Risk