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Add HSCIC-CPT conditional independence test #270

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Add HSCIC-CPT conditional independence test #270

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6 changes: 4 additions & 2 deletions causallearn/search/ConstraintBased/PC.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,11 +72,12 @@ def pc_alg(
node_names: Shape [n_features]. The name for each feature (each feature is represented as a Node in the graph, so it's also the node name)
alpha : float, desired significance level of independence tests (p_value) in (0, 1)
indep_test : str, the name of the independence test being used
["fisherz", "chisq", "gsq", "kci"]
["fisherz", "chisq", "gsq", "kci", "hscic_cpt"]
- "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
- "hscic_cpt": HSCIC with the conditional permutation test
stable : run stabilized skeleton discovery if True (default = True)
uc_rule : how unshielded colliders are oriented
0: run uc_sepset
Expand All @@ -102,6 +103,8 @@ def pc_alg(
"""

start = time.time()
if indep_test == hscic_cpt:
kwargs.setdefault("hscic_alpha", alpha)
indep_test = CIT(data, indep_test, **kwargs)
cg_1 = SkeletonDiscovery.skeleton_discovery(data, alpha, indep_test, stable,
background_knowledge=background_knowledge, verbose=verbose,
Expand Down Expand Up @@ -502,4 +505,3 @@ def matrix_diff(cg1: CausalGraph, cg2: CausalGraph) -> (float, List[Tuple[int, i
diff_ls.append((i, j))
count += 1
return count / 2, diff_ls

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