Quaternary Class Transformation

The Quaternary Class Transformation Approach (Response Transformation Approach).

Based on

Kuchumov, A. pyuplift: Lightweight uplift modeling framework for Python. (2019). URL: https://github.com/duketemon/pyuplift. License: https://github.com/duketemon/pyuplift/blob/master/LICENSE.

Kane, K., Lo, VSY., and Zheng, J. (2014). “Mining for the truly responsive customers and prospects using truelift modeling: Comparison of new and existing methods”. In:Journal of Marketing Analytics 2(4), 218–238. URL: https://link.springer.com/article/10.1057/jma.2014.18

Devriendt, F. et al. (2018). A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics. Big Data, Vol. 6, No. 1, March 1, 2018, pp. 1-29. Codes found at: data-lab.be/downloads.php.

Contents

QuaternaryTransformation Class

_quaternary_transformation, _quaternary_regularization, fit, predict (not available at this time), predict_proba

class causeinfer.standard_algorithms.quaternary_transformation.QuaternaryTransformation(model=None, regularize=False)

_quaternary_transformation(y, w)

Assigns known quaternary (TP, CP, CN, TN) classes to units.

Parameters:

ynumpy.ndarray(num_units,)int, float

Vector of unit responses.

wnumpy.ndarray(num_units,)int, float

Vector of original treatment allocations across units.

Returns:

np.array(y_transformed)np.array

an array of transformed unit classes.

_quaternary_regularization(y=None, w=None)

Regularization of quaternary classes is based on their treatment assignment.

Parameters:

ynumpy.ndarray(num_units,)int, float

Vector of unit responses.

wnumpy.ndarray(num_units,)int, float

Vector of original treatment allocations across units.

Returns:

control_count, treatment_countint

Regularized amounts of control and treatment classes.

fit(X, y, w)

Trains a model given covariates, responses and assignments.

Parameters:

Xnumpy.ndarray(num_units, num_features)int, float

Matrix of covariates.

ynumpy.ndarray(num_units,)int, float

Vector of unit responses.

wnumpy.ndarray(num_units,)int, float

Vector of original treatment allocations across units.

Returns:

selfcauseinfer.standard_algorithms.QuaternaryTransformation

A trained model.

predict_proba(X)

Predicts the probability that a subject will be a given class given covariates.

Parameters:

Xnumpy.ndarray(num_units, num_features)int, float

New data on which to make predictions.

Returns:

probasnumpy.ndarray(num_units, 2)float

Predicted probabilities for being a favorable class and an unfavorable class.