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Titel:

Causal Discovery with Unobserved Confounding and Non-Gaussian Data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Wang, Y. Samuel; Drton, Mathias
Abstract:
We consider recovering causal structure from multivariate observational data. We assume the data arise from a linear structural equation model (SEM) in which the idiosyncratic errors are allowed to be dependent in order to capture possible latent confounding. Each SEM can be represented by a graph where vertices represent observed variables, directed edges represent direct causal effects, and bidirected edges represent dependence among error terms. Specifically, we assume that the true model cor...     »
Stichworte:
Causal discovery, Graphical model, Latent variables, Non-Gaussian data, Structural equation model
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Journal of Machine Learning Research
Jahr:
2023
Band / Volume:
24
Jahr / Monat:
2023-08
Quartal:
3. Quartal
Monat:
Aug
Heft / Issue:
271
Seitenangaben Beitrag:
1−61
Sprache:
en
Volltext / DOI:
doi:10.48550/arXiv.2007.11131
WWW:
Journal of Machine Learning Research
Status:
Erstveröffentlichung
Eingereicht (bei Zeitschrift):
01.11.2021
Publikationsdatum:
01.08.2023
Semester:
SS 23
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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