3 papers
stat.ME2026
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…
stat.ML2025
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…
math.MG2024
Probability Metrics for Tropical Spaces of Different Dimensions
Roan Talbut, Daniele Tramontano, Yueqi Cao +2
The problem of comparing probability distributions is at the heart of many tasks in statistics and machine learning. Established comparison methods treat the standard setting that…