5 papers
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…
Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
Mathias Drton, Marina Garrote-López, Niko Nikov +2
The paradigm of linear structural equation modeling readily allows one to incorporate causal feedback loops in the model specification. These appear as directed cycles in the commo…
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups…