SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.
Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI) , pages =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
SCOPE implements a relaxed sparsest-permutation approach for scalable causal structure learning that recovers Markov equivalence classes up to 10k variables using incomplete Cholesky factorization on screened supports.