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Title:

Dual Likelihood for Causal Inference under Structure Uncertainty

Document type:
Konferenzbeitrag
Contribution type:
Vortrag / Präsentation
Author(s):
Strieder, David; Drton, Mathias
Pages contribution:
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...     »
Keywords:
linear structural causal models, graphical models, dual likelihood, causal effects, un- certainty quantification
Dewey Decimal Classification:
510 Mathematik
Editor:
MLResearchPress
Book / Congress title:
Proceedings of Machine Learning Research
Congress (additional information):
Third Conference on Causal Learning and Reasoning
Volume:
236
Date of congress:
April 1-3, 2024
Date of publication:
19.03.2024
Year:
2024
Quarter:
1. Quartal
Year / month:
2024-03
Month:
Mar
E-ISBN:
2640-3498
Language:
en
WWW:
Proceedings of Machine Learning Research
Semester:
WS 23-24
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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