ImpliedCIs#

class pgmpy.metrics.ImpliedCIs(ci_test=None, show_progress=True)[source]#

Bases: _BaseUnsupervisedMetric

Tests the implied Conditional Independences (CI) of the DAG in the given data.

Each missing edge in a model structure implies a CI statement. If the distribution of the data is faithful to the constraints of the model structure, these CI statements should hold in the data as well. This function runs statistical tests for each implied CI on the given data.

Parameters:
ci_test: str or callable

The CI test to use for statistical testing. Can be a string name of any test in pgmpy.ci_tests (e.g. "chi_square", "pearsonr") or a callable.

show_progress: bool (default: True)

Whether to show the progress of testing.

Returns:
pd.DataFrame: Returns a dataframe with each implied CI of the model and a p-value

corresponding to it from the statistical test. A low p-value (e.g. <0.05) represents that the CI does not hold in the data.

Examples

>>> from pgmpy.example_models import load_model
>>> from pgmpy.metrics import ImpliedCIs
>>> model = load_model("bnlearn/cancer")
>>> df = model.simulate(int(1e3))
>>> implied_cis = ImpliedCIs(ci_test="chi_square", show_progress=False)
>>> implied_cis.evaluate(X=df, causal_graph=model)
       u         v cond_vars   p-value
0  Pollution    Smoker        []  0.189851
1  Pollution      Xray  [Cancer]  0.404149
2  Pollution  Dyspnoea  [Cancer]  0.613370
3     Smoker      Xray  [Cancer]  0.352665
4     Smoker  Dyspnoea  [Cancer]  1.000000
5       Xray  Dyspnoea  [Cancer]  0.888619
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