causal_testing.estimation.logistic_regression_estimator
This module contains the LogisticRegressionEstimator class for estimating categorical outcomes.
Module Contents
Classes
A Logistic Regression Estimator is a parametric estimator which restricts the variables in the data to a linear |
Attributes
- causal_testing.estimation.logistic_regression_estimator.logger
- class causal_testing.estimation.logistic_regression_estimator.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:
causal_testing.estimation.abstract_regression_estimator.RegressionEstimatorA 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.
- regressor
- 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.
- 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.
- Returns:
The odds ratio. Confidence intervals are not yet supported.