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Titel:

Causal Structural Learning via Local Graphs

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Chen, Wenyu; Drton, Mathias; Shojaie, Ali
Abstract:
We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well as selection bias. Based on structure found in common families of large random networks, we propose a new local notion of sparsity for structure learning in the presence of latent and selection variables, and develop a new version of the fast causal inference (FCI) algorithm, which we refer to as local FCI (lFCI). Unde...     »
Stichworte:
causal inference; structural learning; graphical models; latent variable; high dimensional; algorithm
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
SIAM Journal on Mathematics of Data Science
Jahr:
2023
Band / Volume:
5
Jahr / Monat:
2023-05
Quartal:
2. Quartal
Monat:
May
Heft / Issue:
2
Seitenangaben Beitrag:
280-305
Sprache:
en
Volltext / DOI:
doi:10.1137/20m1362796
Verlag / Institution:
Society for Industrial & Applied Mathematics (SIAM)
E-ISSN:
2577-0187
Publikationsdatum:
12.05.2023
Semester:
SS 23
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
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