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View Code? Open in Web Editor NEWCausal discovery and causal inference tool.
License: Other
Causal discovery and causal inference tool.
License: Other
Hi,
First, thank you for the very informative pdf that is part of the code!
Second, when I run your code (I ran Example 3 as you provided with no changes), I get the following errors (is there a way to specific that continuous data is being used):
The system will generate an approximate target graph using the greedy hill climbing algorithm.
[pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variable is continuous: alcoholism
[pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variable is continuous: alcoholism
Running causal discovery on features selected by LINEAR_REGRESSION
[pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variables are continuous: platelet, hbeag
[pyAgrum] Wrong type: Counts cannot be performed on continuous variables. Unfortunately the following variables are continuous: platelet, hbeag
and lastly, when it reached the line:
latent_edges.extend(self.algo_runner.algo_miic(df_reduced))
it stopped execution because of the error:
latent_edges.extend(self.algo_runner.algo_miic(df_reduced))
TypeError: 'NoneType' object is not iterable
Any suggestions for resolving the. above errors?
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