causallib.estimation.MarginalOutcomeEstimator#

class MarginalOutcomeEstimator(learner, use_stabilized=False, *args, **kwargs)[source]#

A marginal outcome predictor. Assumes the sample is marginally exchangeable, and therefore does not correct (adjust, control) for covariates. Predicts the outcome/effect as if the sample came from a randomized control trial: $Pr[Y|A]$.

Parameters:
compute_weight_matrix(X, a, use_stabilized=None, **kwargs)[source]#

Computes individual weight across all possible treatment values. f(Pr[A=a_j | X_i]) for all individual i and treatment j.

Parameters:
Returns:

A matrix of size (num_subjects, num_treatments) with weight for every individual and every

treatment.

Return type:

pandas.DataFrame

compute_weights(X, a, treatment_values=None, use_stabilized=None, **kwargs)[source]#

Computes individual weight given the individual’s treatment assignment. f(Pr[A=a_i | X_i]) for each individual i.

Parameters:

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

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

  • treatment_values (Any | None) – A desired value/s to extract weights to (i.e. weights to what treatment value should be calculated). If not specified, then the weights are chosen by the individual’s actual treatment assignment.

  • use_stabilized (bool) – Whether to re-weigh the learned weights with the prevalence of the treatment. This overrides the use_stabilized parameter provided at initialization. See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title

  • **kwargs

Returns:

A vector of size (num_subjects,) with a weight for each individual

Return type:

pandas.Series

fit(X=None, a=None, y=None)[source]#

Dummy implementation to match the API. MarginalOutcomeEstimator acts as a WeightEstimator that weights each sample as 1

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,).

Returns:

a fitted model.

Return type:

MarginalOutcomeEstimator

estimate_population_outcome(X, a, y, w=None, treatment_values=None)[source]#

Calculates potential population outcome for each treatment value.

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,).

  • w (pandas.Series | None) – Individual (sample) weights calculated. Used to achieved unbiased average outcome. If not provided, will be calculated on the data.

  • treatment_values (Any) – Desired treatment value/s to stratify upon before aggregating individual into population outcome. If not supplied, calculates for all available treatment values.

Returns:

Series which index are treatment values, and the values are numbers - the

aggregated outcome for the strata of people whose assigned treatment is the key.

Return type:

pandas.Series[Any, float]

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

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.

Returns:

self – The updated object.

Return type:

object