identify.ImplicationTestResult
Results of testing implied conditional independences against data.
Usage
identify.ImplicationTestResult(
results,
alpha,
)Each row corresponds to one implied independence statement from the DAG, tested via partial correlation. A low p-value indicates that the data are inconsistent with the independence — suggesting the DAG may be missing an edge.
Parameters
results: pd.DataFrame-
One row per independence test with columns:
x,y,conditioning_set,partial_corr,p_value,significant. alpha: float-
Significance level used for the
significantcolumn.
Attributes
| Name | Description |
|---|---|
| n_tests | Total number of implied independences tested. |
| n_violations | Number of independence violations (significant partial correlations). |
| violations | Subset of results where the independence is violated. |
n_tests
Total number of implied independences tested.
n_tests: int
n_violations
Number of independence violations (significant partial correlations).
n_violations: int
violations
Subset of results where the independence is violated.
violations: pd.DataFrame
Methods
| Name | Description |
|---|---|
| to_dataframe() | Return the full results as a DataFrame. |
to_dataframe()
Return the full results as a DataFrame.
Usage
to_dataframe()