A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
In complex multivariate systems, interactions among variables are defined by dependency structures, often encoded as directed acyclic graphs ($\text{DAGs}$). However, dependency structures can vary across subjects, and ignoring this structural heterogeneity introduces bias and obscures subpopulation-specific dependencies. To address this, we propose Directed Acyclic Graph-based Dependency Clustering via Alternating Direction Method of Multipliers (DAG-DC-ADMM), a unified framework built upon Structural Equation Modeling (SEM) that jointly learns cluster assignments and cluster-specific dependency structures. We encode acyclicity via a smooth constraint and integrate a groupwise truncated ...
| Authors | Honglin Du, Muxuan Liang, Xiang Zhong |
|---|---|
| Primary category | stat.ML |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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