causallib.estimation.WeightedStandardization#

class WeightedStandardization(outcome_model, weight_model, outcome_covariates=None, weight_covariates=None)[source]#

This model uses the weights from the weight-model (e.g. inverse probability weighting) as individual weights for fitting the outcome model.

References

Parameters:

  • outcome_model (IndividualOutcomeEstimator) – A causal model that estimate on individuals level (e.g. Standardization).

  • weight_model (WeightEstimator) – A causal model for weighting individuals (e.g. IPW).

  • outcome_covariates (numpy.ndarray) – Covariates to use for outcome model. If None - all covariates passed will be used. Either list of column names or boolean mask.

  • weight_covariates (numpy.ndarray) – Covariates to use for weight model. If None - all covariates passed will be used. Either list of column names or boolean mask.

estimate_individual_outcome(X, a, treatment_values=None, predict_proba=None)[source]#

Estimates individual outcome under different treatment values (interventions)

Parameters:

  • X (pandas.DataFrame) – Covariate matrix of size (num_subjects, num_features).

  • a (pandas.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:

pandas.DataFrame

fit(X, a, y, refit_weight_model=True, **kwargs)[source]#

Trains a causal model from observed data.

Parameters:

  • X (pandas.DataFrame) – Covariate matrix of size (num_subjects, num_features).

  • a (pandas.Series) – Treatment assignment of size (num_subjects,).

  • y (pandas.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:

IndividualOutcomeEstimator

set_fit_request(*, a='$UNCHANGED$', refit_weight_model='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • a (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for a parameter in fit.

  • refit_weight_model (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for refit_weight_model parameter in fit.

Returns:

self – The updated object.

Return type:

object