causal_testing.testing.causal_test_adequacy
This module contains code to measure various aspects of causal test adequacy.
Module Contents
Classes
Measures the adequacy of a given DAG by hos many edges and independences are tested. |
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Measures the adequacy of a given test according to the Fisher kurtosis of the bootstrapped result. |
Attributes
- causal_testing.testing.causal_test_adequacy.logger
- class causal_testing.testing.causal_test_adequacy.DAGAdequacy(causal_dag: causal_testing.specification.causal_dag.CausalDAG, test_suite: list[causal_testing.testing.causal_test_case.CausalTestCase])
Measures the adequacy of a given DAG by hos many edges and independences are tested.
- measure_adequacy()
Calculate the adequacy measurement, and populate the dag_adequacy field.
- to_dict()
Returns the adequacy object as a dictionary.
- class causal_testing.testing.causal_test_adequacy.DataAdequacy(test_case: causal_testing.testing.causal_test_case.CausalTestCase, bootstrap_size: int = 100, group_by=None)
Measures the adequacy of a given test according to the Fisher kurtosis of the bootstrapped result.
Positive kurtoses indicate the model doesn’t have enough data so is unstable.
Negative kurtoses indicate the model doesn’t have enough data, but is too stable, indicating that the spread of inputs is insufficient.
Zero kurtosis is optimal.
- measure_adequacy()
Calculate the adequacy measurement, and populate the data_adequacy field.
- to_dict()
Returns the adequacy object as a dictionary.