  Numerous approaches have been proposed to discover causal dependencies in
machine learning and data mining; among them, the state-of-the-art VAR-LiNGAM
(short for Vector Auto-Regressive Linear Non-Gaussian Acyclic Model) is a
desirable approach to reveal both the instantaneous and time-lagged
relationships. However, all the obtained VAR matrices need to be analyzed to
infer the final causal graph, leading to a rise in the number of parameters. To
address this issue, we propose the CGP-LiNGAM (short for Causal Graph
Process-LiNGAM), which has significantly fewer model parameters and deals with
only one causal graph for interpreting the causal relations by exploiting Graph
Signal Processing (GSP).
