3 papers
stat.ML2024
Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
Daniela Schkoda, Elina Robeva, Mathias Drton
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 possib…
math.ST2024
Conditional Independence in Stationary Diffusions
Tobias Boege, Mathias Drton, Benjamin Hollering +3
Stationary distributions of multivariate diffusion processes have recently been proposed as probabilistic models of causal systems in statistics and machine learning. Motivated by…
stat.ME2023
Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models
Daniela Schkoda, Mathias Drton
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 ha…