2 citations · 4 across the 10 of their papers we have counts for
11 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…
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…
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models
Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton
Causal discovery amounts to learning a directed acyclic graph (DAG) that encodes a causal model. This model selection problem can be challenging due to its large combinatorial sear…
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…
Causal Effect Identification in LiNGAM Models with Latent Confounders
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar +2
We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the…
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…
Learning Linear Gaussian Polytree Models with Interventions
D. Tramontano, L. Waldmann, M. Drton +1
We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known interve…
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…