Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent

DOI10.1080/01621459.2012.754359OpenAlexW2081225729MaRDI QIDQ4916947

Fei Fu, Qing Zhou

Publication date: 26 April 2013

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2012.754359

zbMATH Keywords

experimental datapenalized likelihoodstructure learning\(L_1\) regularizationadaptive lasso

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (18)

Tests for differential Gaussian Bayesian networks based on quadratic inference functions ⋮ Causal network learning with non-invertible functional relationships ⋮ Gaussian Bayesian network comparisons with graph ordering unknown ⋮ Partitioned hybrid learning of Bayesian network structures ⋮ Estimation of joint directed acyclic graphs with lasso family for gene networks ⋮ Structure learning of sparse directed acyclic graphs incorporating the scale-free property ⋮ Structural factor equation models for causal network construction via directed acyclic mixed graphs ⋮ Inferring large graphs using \(\ell_1\)-penalized likelihood ⋮ Likelihood Ratio Tests for a Large Directed Acyclic Graph ⋮ Sparse directed acyclic graphs incorporating the covariates ⋮ Reconstruction of a directed acyclic graph with intervention ⋮ Solution path clustering with adaptive concave penalty ⋮ Model selection and local geometry ⋮ Bayesian nonlinear model selection for gene regulatory networks ⋮ Penalized Estimation of Directed Acyclic Graphs From Discrete Data ⋮ Estimation of sparse directed acyclic graphs for multivariate counts data ⋮ Unnamed Item ⋮ Unnamed Item

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