Learning linear non-Gaussian graphical models with multidirected edges

Liu Yiheng, Robeva Elina () and Wang Huanqing
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Liu Yiheng: Department of Mathematics, The University of British Columbia, BC V6T 1Z2, Vancouver, Canada
Robeva Elina: Department of Mathematics, The University of British Columbia, BC V6T 1Z2, Vancouver, Canada
Wang Huanqing: Department of Mathematics, The University of British Columbia, BC V6T 1Z2, Vancouver, Canada

Journal of Causal Inference, 2021, vol. 9, issue 1, 250-263

Abstract: In this article, we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model with given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning multidirected edges rather than only directed and bidirected ones. Multidirected edges appear when more than two of the observed variables have a hidden common cause. We detect the presence of such hidden causes by looking at higher order cumulants and exploiting the multi-trek rule. Our method recovers the correct structure when the underlying graph is a bow-free acyclic mixed graph with potential multidirected edges.

Keywords: graphical models; linear structural equation models; non-Gaussian variables; multi-treks; high-order cumulants (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:9:y:2021:i:1:p:250-263:n:12

DOI: 10.1515/jci-2020-0027

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