MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors. Motivated by an intentionally misspecified non-Gaussian SEM with all Laplace errors, we first introduce the mean absolute residual risk, defined over the space of all real matrices, and show that, asymptotically, the risk of the true weighted causal DAG matrix is strictly smaller than that of any other matrix. Nevertheless, to enhance generality and account for high-dimensional and finite-sample settings, we further incorporate row-specific sparsity penalties along with a soft DAG constraint to derive a continuous sco...
| Authors | Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni |
|---|---|
| Primary category | stat.ML |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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