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Document type:
Zeitungsartikel 
Author(s):
Amendola, C., Dettling, P., Drton, M., Onori,F., and Wu, J. 
Title:
Structure Learning for Cyclic Linear Causal Models 
Abstract:
We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feed-back loops and effects of latent confounders. Generalizing related work on bow-free acyclic graphs, we assume that the underlying graph is simple. This entails that any two observed variables can be related through at most one direct causal effect and that (confounding-induced) correlation between error terms in structural...    »
 
Dewey Decimal Classification:
510 Mathematik 
Congress title:
36th Conference on Uncertainty in Artificial Intelligence (UAI) 
Journal title:
Proceedings of Machine Learning Research (PMLR) 
Year:
2020 
Journal volume:
124 
Year / month:
2020-08 
Quarter:
3. Quartal 
Month:
Aug 
Pages contribution:
999-1008 
Language:
en 
Print-ISSN:
2640-3498 
Date of publication:
03.08.2020 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text