causallib.estimation.PropensityMatching#

class PropensityMatching(learner, **kwargs)[source]#

Matching on propensity score only.

This is a convenience class to execute the common task of propensity score matching. It shares all of the methods of the Matching class but offers a shortcut for initialization.

Parameters:

  • learner (sklearn.estimator) – a trainable propensity model that implements fit and predict_proba. Will be passed to the PropensityTransformer object.

  • **kwargs – see Matching.__init__ for supported kwargs.

__init__(learner, **kwargs)[source]#

Matching on propensity score only.

This is a convenience class to execute the common task of propensity score matching. It shares all of the methods of the Matching class but offers a shortcut for initialization.

Parameters:

  • learner (sklearn.estimator) – a trainable propensity model that implements fit and predict_proba. Will be passed to the PropensityTransformer object.

  • **kwargs – see Matching.__init__ for supported kwargs.

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