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Titel:

Confidence in causal discovery with linear causal models

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
Strieder, David; Freidling, Tobias; Haffner, Stefan; Drton, Mathias
Seitenangaben Beitrag:
1217-1226
Abstract:
Structural causal models postulate noisy functional relations among a set of interacting variables. The causal structure underlying each such model is naturally represented by a directed graph whose edges indicate for each variable which other variables it causally depends upon. Under a number of different model assumptions, it has been shown that this causal graph and, thus also, causal effects are identifiable from mere observational data. For these models, practical algorithms have been devis...     »
Dewey-Dezimalklassifikation:
510 Mathematik
Herausgeber:
MLResearchPress
Kongress- / Buchtitel:
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
Band / Teilband / Volume:
161
Datum der Konferenz:
27-30 July 2021
Publikationsdatum:
01.07.2021
Jahr:
2021
Quartal:
3. Quartal
Jahr / Monat:
2021-07
Monat:
Jul
Sprache:
en
Erscheinungsform:
WWW
WWW:
Proceedings of Machine Learning Research
Semester:
SS 21
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
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