:py:mod:`causal_testing.testing.causal_test_adequacy`
=====================================================

.. py:module:: causal_testing.testing.causal_test_adequacy

.. autoapi-nested-parse::

   This module contains code to measure various aspects of causal test adequacy.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.testing.causal_test_adequacy.DAGAdequacy
   causal_testing.testing.causal_test_adequacy.DataAdequacy




Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.testing.causal_test_adequacy.logger


.. py:data:: logger

   

.. py:class:: 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.

   .. py:method:: measure_adequacy()

      Calculate the adequacy measurement, and populate the `dag_adequacy` field.


   .. py:method:: to_dict()

      Returns the adequacy object as a dictionary.



.. py:class:: 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.

   .. py:method:: measure_adequacy()

      Calculate the adequacy measurement, and populate the data_adequacy field.


   .. py:method:: to_dict()

      Returns the adequacy object as a dictionary.