Extracting informative variables in the validation of two-group causal relationship
Journal
Computational Statistics
Journal Volume
28
Journal Issue
3
Pages
1151-1167
Date Issued
2013
Author(s)
Abstract
The validation of causal relationship between two groups of multivariate time series data often requires the precedence knowledge of all variables. However, in practice one finds that some variables may be negligible in describing the underlying causal structure. In this article we provide an explicit definition of "non-informative variables" in a two-group causal relationship and introduce various automatic computer-search algorithms that can be utilized to extract informative variables based on a hypothesis testing procedure. The result allows us to represent a simplified causal relationship by using minimum possible information on two groups of variables. © 2012 Springer-Verlag.
Subjects
Automatic computer-search algorithm; Causal relationship; Informative variables; Modified Wald test; Vector autoregression model
SDGs
Type
journal article