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stat.ME2026
Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models
Penggang Gao, Ming Cai, Hisayuki Hara
Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain li…
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…