Learning Linear Gaussian Polytree Models With Interventions

Tramontano, Daniele; Waldmann, L.; Drton, M.; Duarte, Eliana

doi:10.1109/jsait.2023.3328429

Cited by 1 publication

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inferring causal relationships between observed variables with latent variables is of significant importance and has been applied in many fields (Sachs et al 2005; Wang and Drton 2020;Tramontano, Monod, and Drton 2022;Morioka and Hyvarinen 2023). The Latent Variables Linear Non-Gaussian Acyclic Model (LvLiNGAM) (Hoyer et al 2008b;Entner and Hoyer 2011;Tashiro et al 2014) is one of the most prominent approaches for this problem.…”

Section: Introductionmentioning

confidence: 99%

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants

Chen,

Huang,

Cai

et al. 2024

AAAI

View full text Add to dashboard Cite

Causal discovery with latent variables is a crucial but challenging task. Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between. Interestingly, we notice that this structure can be identified through the utilization of higher-order cumulants. By leveraging the higher-order cumulants of non-Gaussian data, we provide an analytical solution for estimating the causal coefficients or their ratios. With the estimated (ratios of) causal coefficients, we propose a novel approach to identify the existence of a causal edge between two observed variables subject to latent variable influence. In case when such a causal edge exits, we introduce an asymmetry criterion to determine the causal direction. The experimental results demonstrate the effectiveness of our proposed method.

show abstract