causallib.estimation.standardization module
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Created on Apr 25, 2018
- class causallib.estimation.standardization.Standardization(learner, encode_treatment=False, predict_proba=False)[source]
Bases:
causallib.estimation.base_estimator.IndividualOutcomeEstimatorStandard standardization model for causal inference. Learns a model that takes into account the treatment assignment, and later, this value can be intervened, changing the predicted outcome.
- Parameters
learner – Initialized sklearn model.
encode_treatment (bool) – Whether to encode the treatment as one-hot matrix. Usually good if n_treatment > 2.
predict_proba (bool) – In case the outcome task is classification and in case learner supports the operation, if True - prediction will utilize learner’s predict_proba or decision_function which returns a continuous matrix of size (n_samples, n_classes). If False - predict will be used and return value will be based on a vector of class classifications.
- estimate_individual_outcome(X, a, treatment_values=None, predict_proba=None)[source]
Estimates individual outcome under different treatment values (interventions)
- Parameters
X (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
a (pd.Series) – Treatment assignment of size (num_subjects,).
treatment_values (Any) – Desired treatment value/s to use when estimating the counterfactual outcome/ If not supplied, calculates for all available treatment values.
predict_proba (bool | None) – In case the outcome task is classification and in case learner supports the operation, if True - prediction will utilize learner’s predict_proba or decision_function which returns a continuous matrix of size (n_samples, n_classes). If False - predict will be used and return value will be based on a vector of class classifications. If None - parameter is ignored and behaviour is as specified when initializing the IndividualOutcomeEstimator.
- Returns
- DataFrame which columns are treatment values and rows are individuals: each column is a vector
size (num_samples,) that contains the estimated outcome for each individual under the treatment value in the corresponding key.
- Return type
pd.DataFrame
- fit(X, a, y, sample_weight=None)[source]
Trains a causal model from observed data.
- Parameters
X (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
a (pd.Series) – Treatment assignment of size (num_subjects,).
y (pd.Series) – Observed outcome of size (num_subjects,).
sample_weight – To be passed to the underlining scikit-learn’s fit method.
- Returns
A causal weight model with an inner learner fitted.
- Return type
- class causallib.estimation.standardization.StratifiedStandardization(learner, treatment_values=None, predict_proba=False)[source]
Bases:
causallib.estimation.base_estimator.IndividualOutcomeEstimatorStandardization model that learns a model for each treatment group (i.e. subgroup of subjects with the same treatment assignment).
- Parameters
learner – Initialized sklearn model or a mapping (dict) between treatment value and initialized model, For example: {0: Ridge(alpha=5), 1: Ridge(alpha=0.1)}, or even different models all over: {0: Ridge(), 1: RandomForestRegressor} Make sure these treatment_values keys represent all treatment values found in later use.
treatment_values (list) – list of unique values of treatment (can be a single value as well). If known beforehand (on initialization time), can be passed now to init, otherwise would be inferred during fit (where treatment assignment must be supplied). Make sure these treatment_values represent all treatment values found in later use.
predict_proba (bool) – In case the outcome task is classification and in case learner supports the operation, if True - prediction will utilize learner’s predict_proba or decision_function which returns a continuous matrix of size (n_samples, n_classes). If False - predict will be used and return value will be based on a vector of class classifications.
- estimate_individual_outcome(X, a, treatment_values=None, predict_proba=None)[source]
Estimates individual outcome under different treatment values (interventions)
- Parameters
X (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
a (pd.Series) – Treatment assignment of size (num_subjects,).
treatment_values (Any) – Desired treatment value/s to use when estimating the counterfactual outcome/ If not supplied, calculates for all available treatment values.
predict_proba (bool | None) – In case the outcome task is classification and in case learner supports the operation, if True - prediction will utilize learner’s predict_proba or decision_function which returns a continuous matrix of size (n_samples, n_classes). If False - predict will be used and return value will be based on a vector of class classifications. If None - parameter is ignored and behaviour is as specified when initializing the IndividualOutcomeEstimator.
- Returns
- DataFrame which columns are treatment values and rows are individuals: each column is a vector
size (num_samples,) that contains the estimated outcome for each individual under the treatment value in the corresponding key.
- Return type
pd.DataFrame
- fit(X, a, y, sample_weight=None)[source]
Trains a causal model from observed data.
- Parameters
X (pd.DataFrame) – Covariate matrix of size (num_subjects, num_features).
a (pd.Series) – Treatment assignment of size (num_subjects,).
y (pd.Series) – Observed outcome of size (num_subjects,).
sample_weight – To be passed to the underlining scikit-learn’s fit method.
- Returns
A causal weight model with an inner learner fitted.
- Return type