Perform Peter-Clark (PC 1) algorithm for causal discovery. We also allowed data sets with missing values, for which testwise-deletion PC is included (choosing ‘MV-Fisher_Z” for the test name).
If you would like to use missing-value PC 2, please set ‘mvpc’ as True.
from causallearn.search.ConstraintBased.PC import pc G = pc(data, alpha, indep_test, stable, uc_rule, uc_priority, mvpc, correction_name, background_knowledge) # visualization using pydot cg.draw_pydot_graph() # visualization using networkx # cg.to_nx_graph() # cg.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.
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: False.
correction_name. Missing value correction if using missing-value PC. Default: ‘MV_Crtn_Fisher_Z’
background_knowledge: class BackgroundKnowledge. Add prior edges according to assigned causal connections. For detailed usage, please kindly refer to its usage example.
cg : a CausalGraph object. Nodes in the graph correspond to the column indices in the data.
Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, prediction, and search. MIT press.
Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H., & Zhang, K. (2019, April). Causal discovery in the presence of missing data. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 1762-1770). PMLR.