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

.. py:module:: causal_testing.estimation.logistic_regression_estimator

.. autoapi-nested-parse::

   This module contains the LogisticRegressionEstimator class for estimating categorical outcomes.



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

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.estimation.logistic_regression_estimator.LogisticRegressionEstimator




Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.estimation.logistic_regression_estimator.logger


.. py:data:: logger

   

.. py:class:: LogisticRegressionEstimator(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[causal_testing.specification.variable.Variable, Any] = None, formula: str = None, alpha: float = 0.05, query: str = '')


   Bases: :py:obj:`causal_testing.estimation.abstract_regression_estimator.RegressionEstimator`

   A Logistic Regression Estimator is a parametric estimator which restricts the variables in the data to a linear
   combination of parameters and functions of the variables (note these functions need not be linear). It is designed
   for estimating categorical outcomes.

   .. py:attribute:: regressor

      

   .. py:method:: add_modelling_assumptions()

      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:: estimate_unit_odds_ratio() -> causal_testing.estimation.effect_estimate.EffectEstimate

      Estimate the odds ratio of increasing the treatment by one. In logistic regression, this corresponds to the
      coefficient of the treatment of interest.

      :return: The odds ratio. Confidence intervals are not yet supported.