Learning directed acyclic graphs by determination of candidate causes for discrete variables

DOI10.1080/00949655.2019.1604709OpenAlexW2939005475WikidataQ107540396 ScholiaQ107540396MaRDI QIDQ5107436

D. Plewczyński, Vahid Rezaei Tabar, Hamid Zareifard, Selva Salimi

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1604709

zbMATH Keywords

directed acyclic graphLasso regressioncausal relations\(L1\)-regularized Markov blanket

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• TETRAD

Cites Work

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• The max-min hill-climbing Bayesian network structure learning algorithm
• Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
• Causation, prediction, and search
• Least angle regression. (With discussion)
• Prentice's Approach and the Meta-Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints
• Reducibility among Combinatorial Problems

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