Comments (1)
Thanks for your interest in our work!
Regarding question 1), edges
represent discrete samples, i.e. a binary choice whether or not a certain edge in the graph exists. prob
on the other hand, represents the probability of the edge existing and is therefore a continuous value in the range [0,1].
Regarding question 2), we never actually use the accuracy or AUROC for the training of our model, but only to evaluate its causal discovery performance. And indeed, the summary graph that we predict with ACD is a dense graph that may contain cycles, however, we assume that the unrolled graph over time is a DAG.
Hope this helps!
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Related Issues (8)
- Error running demo HOT 1
- Do you enforce that the predicted causal graphs are the same for all samples in the Netsim experiment? HOT 1
- How do you output the causal graph once the model is trained? HOT 5
- kl_categorical_uniform returns negative value HOT 2
- Cannot validate the experiment for netsim HOT 1
- Graph visualization HOT 1
- f
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