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

.. py:module:: causal_testing.estimation.instrumental_variable_estimator

.. autoapi-nested-parse::

   This module contains the InstrumentalVariableEstimator class, for estimating
   continuous outcomes with unobservable confounding.



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

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.estimation.instrumental_variable_estimator.InstrumentalVariableEstimator




Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.estimation.instrumental_variable_estimator.logger


.. py:data:: logger

   

.. py:class:: InstrumentalVariableEstimator(base_test_case: causal_testing.testing.base_test_case.BaseTestCase, treatment_value: float, control_value: float, adjustment_set: set, instrument: str, df: pandas.DataFrame = None, alpha: float = 0.05, query: str = '')


   Bases: :py:obj:`causal_testing.estimation.abstract_estimator.Estimator`

   Carry out estimation using instrumental variable adjustment rather than conventional adjustment. This means we do
   not need to observe all confounders in order to adjust for them. A key assumption here is linearity.

   .. 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:: iv_coefficient(df) -> float

      Estimate the linear regression coefficient of the treatment on the
      outcome.


   .. py:method:: estimate_coefficient(bootstrap_size=100) -> causal_testing.estimation.effect_estimate.EffectEstimate

      Estimate the unit ate (i.e. coefficient) of the treatment on the
      outcome.