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Causal inference in econometrics
~
Huynh, Van-Nam.
Causal inference in econometrics
Record Type:
Electronic resources : Monograph/item
Title/Author:
Causal inference in econometricsedited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta.
other author:
Huynh, Van-Nam.
Published:
Cham :Springer International Publishing :2016.
Description:
xi, 638 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Econometrics.
Online resource:
http://dx.doi.org/10.1007/978-3-319-27284-9
ISBN:
9783319272849$q(electronic bk.)
Causal inference in econometrics
Causal inference in econometrics
[electronic resource] /edited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta. - Cham :Springer International Publishing :2016. - xi, 638 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.6221860-949X ;. - Studies in computational intelligence ;v. 216..
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
ISBN: 9783319272849$q(electronic bk.)
Standard No.: 10.1007/978-3-319-27284-9doiSubjects--Topical Terms:
182271
Econometrics.
LC Class. No.: HB139
Dewey Class. No.: 330.015195
Causal inference in econometrics
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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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EB HB139 C374 2016
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http://dx.doi.org/10.1007/978-3-319-27284-9
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