AI RESEARCH
BUILD with Precision: Bottom-Up Inference of Linear DAGs
arXiv CS.LG
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ArXi:2512.16111v2 Announce Type: replace Learning the structure of directed acyclic graphs (DAGs) from observational data is a central problem in causal discovery, statistical signal processing, and machine learning. Under a linear Gaussian structural equation model (SEM) with equal noise variances, the problem is identifiable and we show that the ensemble precision matrix of the observations exhibits a distinctive structure that facilitates DAG recovery.