ArticleScientific reports2026
Clustering matrix-object data by correlational structure as proxy causal signals.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Matrix-object clustering addresses samples with multiple records per object. Many existing methods overlook within-object dependencies, which reduces interpretability and mixes heterogeneous regimes. We propose a clustering approach that uses intra-object correlational structure as a proxy for causal signals to separate regimes prior to any formal causal discovery. Each object is transformed into a rank-based correlation representation, enabling standard distance-based clustering while preserving interpretability. On synthetic and real-world datasets, the method yields stable, interpretable clusters that reduce regime mixing. We emphasize the boundary that correlation does not imply causation; correlational patterns are used only as proxy signals under the stated assumptions.
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