causallib.estimation.StratifiedStandardization#

class StratifiedStandardization(learner, treatment_values=None, predict_proba=False)[source]#

Standardization 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.

__init__(learner, treatment_values=None, predict_proba=False)[source]#
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 (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, sample_weight=None)[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='$UNCHANGED$', sample_weight='$UNCHANGED$')#

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.

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

Returns:

self – The updated object.

Return type:

object