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Practical Approaches to Causal Relationship ExplorationIntroduction

Practical Approaches to Causal Relationship Exploration: Introduction [For centuries causal discovery has been an essential task for many disciplines due to the insights provided by causal relationships. However it is difficult and expensive to identify causal relationships with experimental approaches, especially when there are a large number of variables under consideration. Passively observed data thus has become an important source to be searched for causal relationships. The challenge of causal discovery with observational data lies in the fact that statistical associations detected from observational data are not necessarily causal. This book presents a number of computational methods for automated discovery of causal relationships around a given target (response) variable from large observational data sets, including high dimensional data sets. This chapter introduces the problem and the challenges, and outlines the ideas of the methods.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Practical Approaches to Causal Relationship ExplorationIntroduction

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2015
ISBN
978-3-319-14432-0
Pages
1 –7
DOI
10.1007/978-3-319-14433-7_1
Publisher site
See Chapter on Publisher Site

Abstract

[For centuries causal discovery has been an essential task for many disciplines due to the insights provided by causal relationships. However it is difficult and expensive to identify causal relationships with experimental approaches, especially when there are a large number of variables under consideration. Passively observed data thus has become an important source to be searched for causal relationships. The challenge of causal discovery with observational data lies in the fact that statistical associations detected from observational data are not necessarily causal. This book presents a number of computational methods for automated discovery of causal relationships around a given target (response) variable from large observational data sets, including high dimensional data sets. This chapter introduces the problem and the challenges, and outlines the ideas of the methods.]

Published: Mar 3, 2015

Keywords: Causal Relationship; Association Rule; Reading Skill; Association Rule Mining; White Stork

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