Yujia Zheng

Thanks for suggesting this, we have put it on the list and will try to see the feasibility of it.

Yea you are right, at least for these LiNGAM-based methods we could definitely visualize them in a similar way as follows: ``` from causallearn.search.FCMBased import lingam model = lingam.ICALiNGAM() model.fit(data)...

Hi, it seems that #119 and #29 are related to this issue. Could you please try to add some random noises and see if it remains? I conjecture that it...

We had updated the code but perhaps your case was not covered (#58). Would you mind providing us (perhaps via email: [email protected]) with a minimal reproducing example for your issue?

Thanks for reporting. If you would like to, could you please send us a minimal reproducing example of this issue (perhaps via email: [email protected])?

Hi, thanks for your suggestions. Did you happen to try cg.G.graph? It returns an 'adjacency matrix' with its semantics documented [here ](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html) (Returns).

Thanks for the question. In this case, cg.G.graph[5,4] is also -1, which represents that there exists an undirected edge between 4 and 5. I agree with you that this is...

Thanks for the suggestion. I'm not fully sure what would be the best reason to incorporate it as a hyper-parameter of the function. It seems that this type of dictionary...

Hi @theabrusch , sorry for the late reply. Could you please try the latest version (1.3.3) to see if the issue remains?

Hi, as @jdramsey mentioned, this is a studied problem for constraint-based methods including FCI. Please let me know if I misunderstood anything.