PC
Algorithm Introduction
Perform Peter-Clark (PC 1) algorithm for causal discovery. We also allowed data sets with missing values, for which missing-value testwise-deletion PC is included (choosing ‘MV-Fisher_Z” for the test name).
Usage
from causal-learn.search.ConstraintBased.PC import pc
G = pc(data, alpha, indep_test, stable, uc_rule, uc_priority)
Parameters
data: data set (numpy ndarray).
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.
2: run definiteMaxP.
- uc_priority: rule of resolving conflicts between unshielded colliders.
-1: whatever is default in uc_rule.
0: overwrite.
1: orient bi-directed.
2: prioritize existing colliders.
3: prioritize stronger colliders.
4: prioritize stronger* colliders.
Returns
cg : a CausalGraph object.
- 1
Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, prediction, and search. MIT press.
- 2
Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011, July). Kernel-based Conditional Independence Test and Application in Causal Discovery. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 804-813). AUAI Press.