sklift.models.ClassTransformationReg

class sklift.models.models.ClassTransformationReg(estimator, propensity_val=None, propensity_estimator=None)[source]

aka CATE-generating (Conditional Average Treatment Effect) Transformation of the Outcome.

Redefine target variable, which indicates that treatment make some impact on target or did target is negative without treatment: Z = Y * (W - p)/(p * (1 - p)),

where Y - target vector, W - vector of binary communication flags, and p is a propensity score (the probabilty that each y_i is assigned to the treatment group.).

Then, train a regressor on Z to predict uplift.

Returns uplift predictions and optionally propensity predictions.

The propensity score can be a scalar value (e.g. p = 0.5), which would mean that every subject has identical probability of being assigned to the treatment group.

Alternatively, the propensity can be learned using a Classifier model. In this case, the model predicts the probability that a given subject would be assigned to the treatment group.

Read more in the User Guide.

Parameters:

estimator (estimator object implementing 'fit') – The object to use to fit the data.
propensity_val (float) – A constant propensity value, which assumes every subject has equal probability of assignment to the treatment group.
propensity_estimator (estimator object with predict_proba) – The object used to predict the propensity score if propensity_val is not given.

Example:

# import approach
from sklift.models import ClassTransformationReg
# import any estimator adheres to scikit-learn conventions
from sklearn.linear_model import LinearRegression, LogisticRegression


# define approach
ct = ClassTransformationReg(estimator=LinearRegression(), propensity_estimator=LogisticRegression())
# fit the model
ct = ct.fit(X_train, y_train, treat_train)
# predict uplift
uplift_ct = ct.predict(X_val)

References

Athey, Susan & Imbens, Guido & Ramachandra, Vikas. (2015). Machine Learning Methods for Estimating Heterogeneous Causal Effects.

See also

Other approaches:

SoloModel: Single model approach.
TwoModels: Double classifier approach.
ClassTransformation: Binary classifier transformation approach.

fit(X, y, treatment, estimator_fit_params=None)[source]

Fit the model according to the given training data.

Parameters:

X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape (n_samples,)) – Target vector relative to X.
treatment (array-like, shape (n_samples,)) – Binary treatment vector relative to X.
estimator_fit_params (dict, optional) – Parameters to pass to the fit method of the estimator.

Returns:

self

Return type:

object

predict(X)[source]

Perform uplift on samples in X.

Parameters:: X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.
Returns:: uplift
Return type:: array (shape (n_samples,))

predict_propensity(X)[source]

Predict propensity values.

Parameters:: X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.
Returns:: propensity
Return type:: array (shape (n_samples,))

set_fit_request(*, estimator_fit_params: bool | None | str = '$UNCHANGED$', treatment: bool | None | str = '$UNCHANGED$') → ClassTransformationReg

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:

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

Returns:

self – The updated object.

Return type:

object