causaldag.structure_learning.permutation2dag¶
-
causaldag.structure_learning.
permutation2dag
(perm: list, ci_tester: causaldag.utils.ci_tests.ci_tester.CI_Tester, verbose=False, fixed_adjacencies: Set[FrozenSet[Hashable]] = {}, fixed_gaps: Set[FrozenSet[Hashable]] = {}, progress=False)[source]¶ Estimate the minimal IMAP of a DAG which is consistent with the given permutation.
Parameters: - perm – list of nodes representing the permutation.
- ci_tester – object for testing conditional independence.
- verbose – if True, log each CI test.
- fixed_adjacencies – set of nodes known to be adjacent.
- fixed_gaps – set of nodes known not to be adjacent.
Examples
>>> from causaldag.utils.ci_tests import MemoizedCI_Tester, partial_correlation_test, partial_correlation_suffstat >>> perm = [0,1,2] >>> suffstat = partial_correlation_suffstat(samples) >>> ci_tester = MemoizedCI_Tester(partial_correlation_test, suffstat) >>> permutation2dag(perm, ci_tester, fixed_gaps={frozenset({1, 2})})