3 papers
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.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…