Causality in Linear Nongaussian Acyclic Models in the Presence of Latent Gaussian Confounders

DOI10.1162/NECO_a_00444zbMath1418.62310WikidataQ45003761 ScholiaQ45003761MaRDI QIDQ5378207

Zhitang Chen, Lai Wan Chan

Publication date: 12 June 2019

Published in: Neural Computation (Search for Journal in Brave)

zbMATH Keywords

LiNGAMcausal discovery problemslinear nongaussian acyclic models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items

Unnamed Item ⋮ ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders ⋮ Causal Discovery via Reproducing Kernel Hilbert Space Embeddings ⋮ Cyclic structural causal model with unobserved confounder effect

Uses Software

• TETRAD
• DirectLiNGAM

Cites Work

• Estimating exogenous variables in data with more variables than observations
• Information-geometric approach to inferring causal directions
• Hierarchical models of variance sources
• Estimation of causal effects using linear non-Gaussian causal models with hidden variables
• Causation, prediction, and search
• Independent component analysis, a new concept?
• Efficient factor GARCH models and factor-DCC models
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