Learning Linear Gaussian Polytree Models with Interventions

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023-11-30

Autores

Eliana Duarte (Autor) FCUP; Daniele Tramontano (Autor) Outra; Mathias Drton (Autor) Outra; Leonard Walmann (Autor) Outra

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: IEEE Journal on Selected Areas in Information Theory

Nº de Série 2641-8770 Vol. 4

Páginas: 569-578

ISSN: 2641-8770

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-010-67G

DOI: 10.1109/jsait.2023.3328429

Abstract (EN): We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known intervention targets. Our methods first learn the skeleton of the polytree and then orient its edges. The output is a CPDAG representing the interventional equivalence class of the polytree of the true underlying distribution. The skeleton and orientation recovery procedures we use rely on second order statistics and low-dimensional marginal distributions. We assess the performance of our methods under different scenarios in synthetic data sets and apply our algorithm to learn a polytree in a gene expression interventional data set. Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes. © 2020 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9