causallift.nodes package
Submodules
causallift.nodes.estimate_propensity module
causallift.nodes.model_for_each module
- class causallift.nodes.model_for_each.ModelForTreated(*posargs, **kwargs)[source]
Bases:
ModelForTreatedOrUntreated
- class causallift.nodes.model_for_each.ModelForTreatedOrUntreated(treatment_val=1.0)[source]
Bases:
object
- class causallift.nodes.model_for_each.ModelForUntreated(*posargs, **kwargs)[source]
Bases:
ModelForTreatedOrUntreated
- causallift.nodes.model_for_each.bundle_treated_and_untreated_models(treated_model, untreated_model)[source]
causallift.nodes.utils module
- causallift.nodes.utils.add_cate_to_df(args, df, cate_estimated, proba_treated, proba_untreated)[source]
- causallift.nodes.utils.concat_train_test(args, train, test)[source]
Concatenate train and test series. Use series.xs(‘train’) or series.xs(‘test’) to split
- causallift.nodes.utils.concat_train_test_df(args, train, test)[source]
Concatenate train and test data frames. Use df.xs(‘train’) or df.xs(‘test’) to split.
- causallift.nodes.utils.get_cols_features(df, non_feature_cols=['Treatment', 'Outcome', 'TransformedOutcome', 'Propensity', 'Recommendation'])[source]
- causallift.nodes.utils.initialize_model(args, model_key='uplift_model_params', default_estimator='sklearn.linear_model.LogisticRegression')[source]
- Return type:
Type
[BaseEstimator
]
- causallift.nodes.utils.len_to(df, treatment=1.0, outcome=1.0, col_treatment='Treatment', col_outcome='Outcome')[source]
- causallift.nodes.utils.overall_uplift_gain_(df, treatment=1.0, outcome=1.0, col_treatment='Treatment', col_outcome='Outcome')[source]