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Titel:

Rank-Based Causal Discovery for Post-Nonlinear Models

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Keropyan, Grigor; Strieder, David; Drton, Mathias
Abstract:
Learning causal relationships from empirical observations is a central task in scientific research. A common method is to employ structural causal models that postulate noisy functional relations among a set of interacting variables. To ensure unique identifiability of causal directions, researchers consider restricted subclasses of structural causal models. Post-nonlinear (PNL) causal models constitute one of the most flexible options for such restricted subclasses, containing in particular the...     »
Dewey-Dezimalklassifikation:
510 Mathematik
Kongress- / Buchtitel:
Proceedings of Machine Learning Research
Kongress / Zusatzinformationen:
26th International Conference on Artificial Intelligence and Statistics
Band / Teilband / Volume:
206
Datum der Konferenz:
April 25 - April 27, 2023
Verlag / Institution:
MLResearchPress
Publikationsdatum:
24.04.2023
Jahr:
2023
Quartal:
2. Quartal
Jahr / Monat:
2023-04
Monat:
Apr
Seiten:
7849-7870
E-ISBN:
2640-3498
Sprache:
en
Erscheinungsform:
WWW
WWW:
Proceedings of Machine Learning Research
Semester:
SS 23
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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