This page provides an overview on how to choose the most appropriate estimator for your workflow.
Recommended use: For continuous numerical outcomes (e.g. the number of people who are vaccinated).
.. autoclass:: causal_testing.estimation.linear_regression_estimator.LinearRegressionEstimator :members: :exclude-members: from_formula, regressor :undoc-members: :show-inheritance: :noindex:
Recommended use: For binary outcomes (yes/no, true/false, success/failure).
.. autoclass:: causal_testing.estimation.logistic_regression_estimator.LogisticRegressionEstimator :members: :exclude-members: from_formula, regressor :undoc-members: :show-inheritance: :noindex:
Recommended use: For categorical outcomes (e.g. colurs: Red, Green, Blue).
.. autoclass:: causal_testing.estimation.multinomial_regression_estimator.MultinomialRegressionEstimator :members: :exclude-members: from_formula, regressor :undoc-members: :show-inheritance: :noindex:
Recommended use: When dealing with unmeasured confounding using instrumental variables.
.. autoclass:: causal_testing.estimation.instrumental_variable_estimator.InstrumentalVariableEstimator :members: :undoc-members: :show-inheritance: :noindex:
Recommended use: For handling missing data or selection bias using inverse probability of censoring weighting (e.g. time-varying data).
.. autoclass:: causal_testing.estimation.ipcw_estimator.IPCWEstimator :members: :undoc-members: :show-inheritance: :noindex:
- Recommended use: For randomised controlled trials or experimental data where treatment assignment is randomised.
- Directly runs the system under test multiple times with different configurations (e.g. you need to collect new data by executing your system multiple times).
.. autoclass:: causal_testing.estimation.experimental_estimator.ExperimentalEstimator :members: :undoc-members: :show-inheritance: :noindex: