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Springer Causal Inference in Econometrics - Studies in Comp Intel

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Description

This volume from Springer focuses on the complex task of causal inference within the field of econometrics. It addresses one of the most difficult challenges in data analysis: determining whether one observed phenomenon causally influences another, or if both share a common cause. To achieve a deeper understanding of causal inference, the text explores the necessity of accurate economic models. The collection includes research papers that utilize non-traditional economic models, such as fuzzy logic, to improve the precision of these analyses. This book serves as a specialized resource for those studying computational intelligence and economic modeling.

Key Features

This Springer publication focuses on the analysis of causal inference in econometrics.

The text addresses the difficulty of distinguishing between causal influence and common causes in data analysis.

Included research papers utilize non-traditional economic models to increase accuracy.

The volume is part of the Studies in Computational Intelligence series, number 622.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
13 January 2016
Listed Since
22 October 2015

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