Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity
Machine Learning
2022-11-28 v1 Signal Processing
Methodology
Abstract
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).
Cite
@article{arxiv.2211.13800,
title = {Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity},
author = {Aref Einizade and Sepideh Hajipour Sardouie},
journal= {arXiv preprint arXiv:2211.13800},
year = {2022}
}