2 papers
stat.ME2026
Causal invariance in graphical models with latent variables
Marco Borriero, Monia Lupparelli, Giovanni M. Marchetti +1
Causal discovery aims to identify causal relationships among variables from observational or interventional data, typically represented by a directed acyclic graph (DAG). The causa…
stat.ME2025
Gaussian Graphical Models for Partially Observed Multivariate Functional Data
Marco Borriero, Luigi Augugliaro, Gianluca Sottile +1
In many applications, the variables that characterize a stochastic system are measured along a second dimension, such as time. This results in multivariate functional data and the…