:py:mod:`causal_testing.discovery.abstract_discovery`
=====================================================

.. py:module:: causal_testing.discovery.abstract_discovery

.. autoapi-nested-parse::

   This module implements the abstract Discovery class to infer causal DAGs from data.



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

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.discovery.abstract_discovery.Discovery



Functions
~~~~~~~~~

.. autoapisummary::

   causal_testing.discovery.abstract_discovery.simple_cycle
   causal_testing.discovery.abstract_discovery.effect_direction
   causal_testing.discovery.abstract_discovery.is_match



Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.discovery.abstract_discovery.TestResult


.. py:data:: TestResult

   

.. py:function:: simple_cycle(causal_dag: causal_testing.specification.causal_dag.CausalDAG)

   Find a cycle in the given CausalDAG, if one exists, returns the first found.

   :param causal_dag: The CausalDAG to check for cycles.
   :returns: A list of edges in the cycle, or an empty list if there are no cycles.


.. py:function:: effect_direction(result: causal_testing.testing.causal_test_result.CausalTestResult) -> str

   Check whether the estimated causal effect is negative or positive.

   :param result: The causal test result object.
   :returns: Whether the estimated causal test is positive or negative (or no effect).


.. py:function:: is_match(u: str, v: str, patterns: list[str])

   Check whether a given edge matches a given pattern.

   :param u: The origin node of the edge.
   :param v: The destination node of the edge.
   :param patterns: A list of tuples containing the patterns to check against.
   :returns: True if the edge matches the pattern, False otherwise.


.. py:class:: Discovery(df: pandas.DataFrame, random_seed: int = 0, exclude_edges: str = None, include_edges: str = None, alpha: float = 0.05)


   Bases: :py:obj:`abc.ABC`

   Abstract class for causal discovery.

   .. py:method:: discover() -> causal_testing.specification.causal_dag.CausalDAG
      :abstractmethod:

      Discover the causal DAG.

      :returns: The inferred causal DAG.


   .. py:method:: remove_cycles(causal_dag: causal_testing.specification.causal_dag.CausalDAG)

      Remove cycles from individuals by iteratively deleting a random edge from each cycle until there are no more
      cycles.

      :param causal_dag: The CausalDAG to be repaired.


   .. py:method:: write_dot(individual: causal_testing.specification.causal_dag.CausalDAG, output_file: str)

      Write the given individual to the given output file.

      :param individual: The causal DAG to output.
      :param output_file: The name of the file to write to.


   .. py:method:: _json_stub_params(outcome: str) -> str


   .. py:method:: evaluate_tests(causal_dag: causal_testing.specification.causal_dag.CausalDAG) -> pandas.DataFrame

      Generate and evaluate causal test cases from the supplied CausalDAG and return a list of edges for which the
      corresponding causal test case failed.
      These results are then assigned to a new attribute `test_results` within the individual for later reuse.

      :param causal_dag: The CausalDAG to evaluate.
      :returns: Pandas dataframe with test outcome details
                (result, expected effect, treatment, outcome, effect direction).