These ways of analyzing causality reflect the fact that existence is inherently active and dynamic an effect is never purely passive or inert, and a cause works through the inherent activity of the thing affected.
Recently, these methods, collectively referred to as 'the graphical Granger modeling methods', have received considerable attention in the areas of computational biology and data mining, and specifically to address the problem of analyzing causality among gene expressions (Dahlhaus and Eichler, 2003; Mukhopadhyay and Chatterjee, 2007).
The objective of the present article is to demonstrate that leveraging group structure among the temporal variables can indeed help improve their accuracy as methods of Granger graphical modeling, and specifically provide a more effective method for analyzing causality among gene expressions.
Therefore, to the best of our knowledge our paper is the first to analyze the reverse causality between the size of informal sector on the availability of external finance in the formal sector.
As for event causality, we analyze the complexity of the notions of necessary and possible cause, and of the sophisticated notions of weak and actual cause by Halpern and Pearl.
When we discuss the future of East Asia, the crucial standpoint consists in how to face, analyze and study the causality of various empirical phenomena and the mainstreams in all trends.
Both are claimed to analyze some measure of causality in the brain, and both can be used to investigate network function and network dynamics (i.e. information flows).
Granger causality analyzed by COVAIN Sunn and Weckwerth, 2012 [ 38 ]) between metabolites, proteins, and physiological data.
We have developed a novel approach called Partial Granger causality (PGC) to analyze network structures in genes, neurons and brain areas[16].
