3 papers
cs.LG2026
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
Ming Cai, Hisayuki Hara
Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is ident…
cs.LG2025
Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants
Ming Cai, Penggang Gao, Hisayuki Hara
This paper addresses the problem of estimating causal directed acyclic graphs in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM). Existing methods assume mutu…
cs.LG2024
Learning linear acyclic causal model including Gaussian noise using ancestral relationships
Ming Cai, Penggang Gao, Hisayuki Hara
This paper discusses algorithms for learning causal DAGs. The PC algorithm makes no assumptions other than the faithfulness to the causal model and can identify only up to the Mark…