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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.
arXiv:2609.30643v1 Announce Type: new Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wit...
arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gr...
arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.
The paper introduces a method for learning causal structures in linear Gaussian models that may contain directed cycles and an unknown number of latent confounders, bounded by a maximum. It derives the covariance of observed variables, defines marginal quasi-equivalence to identify when different models produce the same observational distributions, and formulates structure learning as a minimization of Gaussian negative log-likelihood with a complexity penalty counting edges and latent variables. Using Bernoulli gates to parameterize edge and latent inclusion, the authors obtain a closed‑form differentiable objective whose expected value shares the same global optimum as the discrete problem, and demonstrate experimentally that this approach yields lower recovery error than prior methods.
arXiv:2607. 03364v1 Announce Type: cross Abstract: We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data.