Perform Peter-Clark algorithm for causal discovery on the augmented data set that captures the unobserved changing factors (CD-NOD, 1).
from causallearn.search.ConstraintBased.CDNOD import cdnod G = cdnod(data, c_indx, alpha, indep_test, stable, uc_rule, uc_priority, mvpc, correction_name) G.to_nx_graph() G.draw_nx_graph(skel=False)
data: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples and n_features is the number of features.
c_indx: time index or domain index that captures the unobserved changing factors.
alpha: desired significance level (float) in (0, 1).
- indep_test: Independence test method function.
“fisherz”: Fisher’s Z conditional independence test.
“chisq”: Chi-squared conditional independence test.
“gsq”: G-squared conditional independence test.
“kci”: kernel-based conditional independence test. (As a kernel method, its complexity is cubic in the sample size, so it might be slow if the same size is not small.)
“mv_fisherz”: Missing-value Fisher’s Z conditional independence test.
stable: run stabilized skeleton discovery if True (default = True).
- uc_rule: how unshielded colliders are oriented.
0: run uc_sepset.
1: run maxP. Orient an unshielded triple X-Y-Z as a collider with an aditional CI test.
2: run definiteMaxP. Orient only the definite colliders in the skeleton and keep track of all the definite non-colliders as well.
- uc_priority: rule of resolving conflicts between unshielded colliders.
-1: whatever is default in uc_rule.
1: orient bi-directed.
2: prioritize existing colliders.
3: prioritize stronger colliders.
4: prioritize stronger* colliders.
mvpc: use missing-value PC or not. Default (and suggested for CDNOD): False.
correction_name. Missing value correction if using missing-value PC. Default: ‘MV_Crtn_Fisher_Z’
cg : a CausalGraph object. Nodes in the graph correspond to the column indices in the data.
Huang, B., Zhang, K., Zhang, J., Ramsey, J. D., Sanchez-Romero, R., Glymour, C., & Schölkopf, B. (2020). Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res., 21(89), 1-53.