4 papers
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…
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…