updated according to feedback from Kun

Author: kunwuz

causallearn/utils/SyntheticDataset.py

@@ -15,8 +15,6 @@ Usage
 
     from causallearn.search.ConstraintBased.FCI import fci
     G = fci(data, indep_test, alpha, verbose=True)
-    G.to_nx_graph()
-    G.draw_nx_graph(skel=False)
 
 Parameters
 -------------------
@@ -37,6 +35,6 @@ and n_features is the number of features.
 
 Returns
 -------------------
-**cg** : a CausalGraph object. Nodes in the graph correspond to the column indices in the data.
+**G** : a GeneralGraph object. Nodes in the graph correspond to the column indices in the data. For visualization, please refer to the `running example <https://github.com/cmu-phil/causal-learn/tree/main/tests>`_.
 
 .. [1] Spirtes, P., Meek, C., & Richardson, T. (1995, August). Causal inference in the presence of latent variables and selection bias. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence (pp. 499-506).

docs/source/search_methods_index/Constrained-based causal discovery methods/FCI.rst

@@ -59,7 +59,7 @@ For detailed usage, please kindly refer to its `usage example <https://github.co
 
 Returns
 -------------------
-**cg** : a CausalGraph object. Nodes in the graph correspond to the column indices in the data.
+**cg** : a CausalGraph object. Nodes in the graph correspond to the column indices in the data. For visualization by networkx, green edges are undirected, blue edges are directed and red edges are bi-directed.
 
 .. [1] Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, prediction, and search. MIT press.
 .. [2] Tu, R., Zhang, C., Ackermann, P., Mohan, K., Kjellström, H., & Zhang, K. (2019, April). Causal discovery in the presence of missing data. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 1762-1770). PMLR.