Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Schkoda, Daniela"'
We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a pair of variables that has been dropped when learning the causal model. To this end, we use the "Leave-One-Variable
Externí odkaz:
http://arxiv.org/abs/2411.05625
We consider linear non-Gaussian structural equation models that involve latent confounding. In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects. Instead, a finite number o
Externí odkaz:
http://arxiv.org/abs/2408.04907
Autor:
Boege, Tobias, Drton, Mathias, Hollering, Benjamin, Lumpp, Sarah, Misra, Pratik, Schkoda, Daniela
Stationary distributions of multivariate diffusion processes have recently been proposed as probabilistic models of causal systems in statistics and machine learning. Motivated by these developments, we study stationary multivariate diffusion process
Externí odkaz:
http://arxiv.org/abs/2408.00583
Autor:
Schkoda, Daniela, Drton, Mathias
The field of causal discovery develops model selection methods to infer cause-effect relations among a set of random variables. For this purpose, different modelling assumptions have been proposed to render cause-effect relations identifiable. One pr
Externí odkaz:
http://arxiv.org/abs/2311.04585