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.
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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.