:py:mod:`causal_testing.estimation.abstract_estimator`
======================================================

.. py:module:: causal_testing.estimation.abstract_estimator

.. autoapi-nested-parse::

   This module contains the Estimator abstract class



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

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.estimation.abstract_estimator.Estimator




Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.estimation.abstract_estimator.logger


.. py:data:: logger

   

.. py:class:: Estimator(base_test_case: causal_testing.testing.base_test_case.BaseTestCase, treatment_value: float, control_value: float, adjustment_set: set, df: pandas.DataFrame = None, effect_modifiers: dict[str, Any] = None, alpha: float = 0.05, query: str = '')


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

   An estimator contains all of the information necessary to compute a causal estimate for the effect of changing
   a set of treatment variables to a set of values.

   All estimators must implement the following two methods:

   1) add_modelling_assumptions: The validity of a model-assisted causal inference result depends on whether
   the modelling assumptions imposed by a model actually hold. Therefore, for each model, is important to state
   the modelling assumption upon which the validity of the results depend. To achieve this, the estimator object
   maintains a list of modelling assumptions (as strings). If a user wishes to implement their own estimator, they
   must implement this method and add all assumptions to the list of modelling assumptions.

   2) estimate_ate: All estimators must be capable of returning the average treatment effect as a minimum. That is, the
   average effect of the intervention (changing treatment from control to treated value) on the outcome of interest
   adjusted for all confounders.

   .. py:method:: add_modelling_assumptions()
      :abstractmethod:

      Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
      must hold if the resulting causal inference is to be considered valid.


   .. py:method:: compute_confidence_intervals() -> list[float, float]

      Estimate the 95% Wald confidence intervals for the effect of changing the treatment from control values to
      treatment values on the outcome.
      :return: 95% Wald confidence intervals.