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arXiv Machine Learning By Sambit Mishra, Urbashi Mitra

Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression

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The paper introduces SURE-Ridge, a closed‑form estimator for recovering the directed acyclic graph of an equal‑variance linear Gaussian structural equation model. It performs parallel node‑wise regressions with regularization parameters selected via Stein's unbiased risk estimate and then applies adaptive thresholding to produce a DAG from a soft adjacency matrix. Experiments show that SURE‑Ridge attains the lowest structural Hamming distance in small‑sample settings and the fastest run time across all tested sample sizes compared to NOTEARS, DAGMA, and GBNSL.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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