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Causal inference in econometrics

Songsak Sriboonchitta.

Causal inference in econometrics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Causal inference in econometrics/ edited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta.
其他作者:	Huynh, Van-Nam.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xi, 638 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Econometrics. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-27284-9
ISBN:	9783319272849

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. 50. .

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

Standard No.: 10.1007/978-3-319-27284-9doiSubjects--Topical Terms:

556981
Econometrics.

LC Class. No.: HB139

Dewey Class. No.: 330.015195

Causal inference in econometrics
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520 $a 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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