Data correlation matrix is singular

So I might be missing some theoretical concept, but want to clear it now.

I have a dataset, take the fork scenario.
My data is generated as follows:

#c) Fork

# Create the graph describing the causal structure
graph = """graph[directed 1 node[id "W" label "W"]
                    node[id "X" label "X"]
                    node[id "Y" label "Y"]
                    edge[source "X" target "Y"]
                    edge[source "X" target "W"]]""".replace('\n', '')


# # Generate the data
X = np.random.randn(N_SAMPLES)
W = 0.5*X
Y = 0.8*X  

# Data to df
df = pd.DataFrame(np.vstack([X, W, Y]).T, columns=['X', 'W', 'Y'])
print(df.head(10))
# Create a model
model = CausalModel(
    data=df,
    treatment=['X'],
    outcome=['Y'],
#     common_causes=['Z'],
    graph=graph
)
plt.figure(figsize=(5,5))
model.view_model()
plt.show()

Clearly, the rank is 1 and you can see in the fig below

When I perform causal discovery using PC, I get 'ValueError: Data correlation matrix is singular. Cannot run fisherz test. Please check your data.'

Below you can find the code which I'm using to perform causal discovery using PC.

from causallearn.search.ConstraintBased.PC import pc
from causallearn.utils.cit import fisherz
from causallearn.utils.GraphUtils import GraphUtils

# default parameters
cg = pc(df.to_numpy(), 0.05, fisherz)

# visualization using pydot
cg.draw_pydot_graph(labels=df.columns)

# or save the graph
pyd = GraphUtils.to_pydot(cg.G, labels=df.columns)
pyd.write_png('pc_fork.png')

Need help in understanding it, although I think as the data is correlated and singular I'm getting this error, however, how can I resolve this error without adding some random noise in the variables W & Y. Isn't causal discovery possible in my case?

Start with the supplied Python reproducer and the causallearn.search.ConstraintBased.PC.pc and causallearn.utils.cit.fisherz entry points, then trace where the singular correlation matrix is rejected. Determine the supported behavior for perfectly dependent variables and add or update coverage so the reproducer has a defined outcome without requiring arbitrary noise.