Learning Linear Gaussian Polytree Models with Interventions

• Type: Article in International Scientific Journal
• Date: 2023-11-30

Authors

Eliana Duarte (Author) FCUP; Daniele Tramontano (Author) Other; Mathias Drton (Author) Other; Leonard Walmann (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: IEEE Journal on Selected Areas in Information Theory

Serial No. 2641-8770 Vol. 4

Pages: 569-578

ISSN: 2641-8770

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9