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Titel:

Dual Likelihood for Causal Inference under Structure Uncertainty

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
Strieder, David; Drton, Mathias
Seitenangaben Beitrag:
1-17
Abstract:
Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postulate noisy functional relations among interacting variables, where the underlying causal structure is then naturally represented by a directed graph whose edges indicate direct causal dependencies. In the typical application, this underlying causal structure must be learned from data, and thus, the remaining structure...     »
Stichworte:
linear structural causal models, graphical models, dual likelihood, causal effects, un- certainty quantification
Dewey-Dezimalklassifikation:
510 Mathematik
Herausgeber:
MLResearchPress
Kongress- / Buchtitel:
Proceedings of Machine Learning Research
Kongress / Zusatzinformationen:
Third Conference on Causal Learning and Reasoning
Band / Teilband / Volume:
236
Datum der Konferenz:
April 1-3, 2024
Publikationsdatum:
19.03.2024
Jahr:
2024
Quartal:
1. Quartal
Jahr / Monat:
2024-03
Monat:
Mar
E-ISBN:
2640-3498
Sprache:
en
WWW:
Proceedings of Machine Learning Research
Semester:
WS 23-24
TUM Einrichtung:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX