PC learns different graphs dependent on the ordering

Hi,

Thank you for your great work on this package!
I am testing the behaviour of the PC algorithm on simple simulated data. I found that the number of directed edges detected differs based on the ordering of the variables given to the algorithm.

I am running the following code:

import numpy as np
import matplotlib.pyplot as plt
from causallearn.search.ConstraintBased.PC import pc

def simulate_data(n_obs):
    '''
    Simulate data from the following graph

        A       B
         \     /
          v   v
            C
          /   \
         v     v
        D       E
    '''
    A = np.random.normal(size = n_obs)
    B = np.random.normal(size = n_obs)
    C = A + B + np.random.normal(size = n_obs)*0.25
    D = C + np.random.normal(size = n_obs)*0.25
    E = C + np.random.normal(size = n_obs)*0.25
    return np.stack((A,B,C,D,E), axis =1)

# generate data
n = 10000
data = simulate_data(n)

# test different permutations
permutations = [[0,1,2,3,4], [0,1,3,2,4]]

for permutation in permutations:
    graph = pc(data[:,permutation], 0.05, 'fisherz', node_names = np.array(["A","B", "C", "D", "E"])[permutation],  verbose = False)
    graph.draw_pydot_graph(labels=np.array(["A","B", "C", "D", "E"])[permutation])
    plt.show()

When the ordering of variables C and D is permuted, the PC algorithm returns the Graph with an undirected edge from C to D. However, when the ordering is unpermuted, the PC algorithm correctly directs the edge from C to D. This happens, even though the p-values in the CI tests are unchanged for the two permutations.
Is this expected behaviour or can you help me fix this?