5 papers
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…
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…
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…
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…
Learning causal graphs using variable grouping according to ancestral relationship
Ming Cai, Hisayuki Hara
Several causal discovery algorithms have been proposed. However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using e…