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, andp
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