2 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Semiparametric Inference for Half-Trek Estimators in Linear Structural Equation Models
Leopold Mareis, Nils Sturma, Mathias Drton
Linear structural equation models on directed mixed graphs encode causal relationships among variables subject to latent confounding. The half-trek criterion (HTC) provides a graph…
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
Tom Hochsprung, Nils Sturma, Jakob Runge +2
We consider linear structural equation models with explicitly modelled latent variables. In such models, observed and latent variables solve linear equations including stochastic n…
Parameter identification in linear non-Gaussian causal models under general confounding
Daniele Tramontano, Mathias Drton, Jalal Etesami
Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of th…
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…
High-Dimensional Causal Discovery Under non-Gaussianity
Y. Samuel Wang, Mathias Drton
We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, , of the variables such that each observed varia…