依赖性混合数据与基因调控网络推论的因果关系发现

Causal Discovery on Dependent Mixed Data with Applications to Gene Regulatory Network Inference

Causal discovery aims to infer causal relationships among variables from observational data, typically represented by a directed acyclic graph (DAG). Most existing methods assume independent and identically distributed observations, an assumption often violated in modern applications. In addition, many datasets contain a mixture of continuous and discrete variables, which further complicates causal modeling when dependence across samples is present. To address these challenges, we propose a de-c