Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity

• URL: http://arxiv.org/abs/2211.13800v1
• Date: Thu, 24 Nov 2022 21:09:55 GMT
• Title: Estimation of a Causal Directed Acyclic Graph Process using Non-Gaussianity
• Authors: Aref Einizade, Sepideh Hajipour Sardouie
• Abstract summary: We propose a new approach to discover causal dependencies in machine learning and data mining. The CGP-LiNGAM has significantly fewer model parameters and deals with only one causal graph for interpreting the causal relations.
• Score: 3.04585143845864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

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