Pessimistic Uplift Transformation

The Pessimistic Uplift 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.

Shaar, A., Abdessalem, T., and Segard, O. (2016). “Pessimistic Uplift Modeling”. ACM SIGKDD, August 2016, San Francisco, California USA, arXiv:1603.09738v1. URL:https://pdfs.semanticscholar.org/a67e/401715014c7a9d6a6679df70175be01daf7c.pdf.

Contents

PessimisticUplift Class

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

class causeinfer.standard_algorithms.pessimistic.PessimisticUplift(model=None)

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.PessimisticUplift

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.