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

.. py:module:: causal_testing.estimation.ipcw_estimator

.. autoapi-nested-parse::

   This module contains the IPCWEstimator class, for estimating the time to a particular event



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

Classes
~~~~~~~

.. autoapisummary::

   causal_testing.estimation.ipcw_estimator.IPCWEstimator




Attributes
~~~~~~~~~~

.. autoapisummary::

   causal_testing.estimation.ipcw_estimator.logger


.. py:data:: logger

   

.. py:class:: IPCWEstimator(df: pandas.DataFrame, timesteps_per_observation: int, control_strategy: list[tuple[int, str, Any]], treatment_strategy: list[tuple[int, str, Any]], outcome: causal_testing.specification.variable.Variable, status_column: str, fit_bl_switch_formula: str, fit_bltd_switch_formula: str, eligibility=None, alpha: float = 0.05, total_time: float = None)


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

   Class to perform Inverse Probability of Censoring Weighting (IPCW) estimation
   for sequences of treatments over time-varying data.

   :param: df: Input DataFrame containing time-varying data.
   :param: timesteps_per_observation: Number of timesteps per observation.
   :param: control_strategy: The control strategy, with entries of the form (timestep, variable, value).
   :param: treatment_strategy: The treatment strategy, with entries of the form (timestep, variable, value).
   :param: outcome: Name of the outcome column in the DataFrame.
   :param: status_column: Name of the status column in the DataFrame, which should be True for operating normally,
                          False for a fault.
   :param: fit_bl_switch_formula: Formula for fitting the baseline switch model.
   :param: fit_bltd_switch_formula: Formula for fitting the baseline time-dependent switch model.
   :param: eligibility: Function to determine eligibility for treatment. Defaults to None for "always eligible".
   :param: alpha: Significance level for hypothesis testing. Defaults to 0.05.
   :param: total_time: Total time for the analysis. Defaults to one plus the length of of the strategy (control or
                       treatment) with the most elements multiplied by `timesteps_per_observation`.

   .. 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:: setup_xo_t_do(individual: pandas.DataFrame, strategy_assigned: list)

      Return a binary sequence with each bit representing whether the current
      index is the time point at which the individual diverted from the
      assigned treatment strategy (and thus should be censored).

      :param individual: DataFrame representing the individual.
      :param strategy_assigned: The assigned treatment strategy.


   .. py:method:: setup_fault_t_do(individual: pandas.DataFrame)

      Return a binary sequence with each bit representing whether the current
      index is the time point at which the event of interest (i.e. a fault) occurred.

      N.B. This is rounded _up_ to the nearest multiple of `self.timesteps_per_observation`.
      That is, if the fault occurs at time 22, and `self.timesteps_per_observation == 5`, then
      `fault_t_do` will be 25.


   .. py:method:: setup_fault_time(individual: pandas.DataFrame, perturbation: float = -0.001)

      Return the time at which the event of interest (i.e. a fault) occurred.


   .. py:method:: preprocess_data()

      Set up the treatment-specific columns in the data that are needed to estimate the hazard ratio.


   .. py:method:: estimate_hazard_ratio() -> causal_testing.estimation.effect_estimate.EffectEstimate

      Estimate the hazard ratio.