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Title:

Learning Linear Gaussian Polytree Models With Interventions

Document type:
Zeitschriftenaufsatz
Author(s):
Tramontano, Daniele; Waldmann, Leonard; Drton, Mathias; Duarte, Eliana .
Abstract:
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-dime...     »
Keywords:
Causal discovery, interventions, linear structural equation model, polytrees
Dewey Decimal Classification:
510 Mathematik
Journal title:
IEEE Journal on Selected Areas in Information Theory
Year:
2023
Journal volume:
4
Year / month:
2023-10
Quarter:
4. Quartal
Month:
Oct
Pages contribution:
569-578
Language:
en
Fulltext / DOI:
doi:10.1109/jsait.2023.3328429
WWW:
IEEE Xplore
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Print-ISSN:
2641-8770
E-ISSN:
2641-8770
Status:
Verlagsversion / published
Date of publication:
01.10.2023
Semester:
WS 23-24
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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