Uplift Regression: The R Package tools4uplift
Abstract
Uplift modeling aims at predicting the causal effect of an action such as a medical treatment or a marketing campaign on a particular individual, by taking into consideration the response to a treatment. The treatment group contains individuals who are subject to an action; a control group serves for comparison. Uplift modeling is used to order the individuals with respect to the value of a causal effect, e.g., positive, neutral, or negative. Though there are some computational methods available for uplift modeling, most of them exclude statistical regression models. The R Package tools4uplift intends to fill this gap. This package comprises tools for: i) quantization, ii) visualization, iii) feature selection, iv) parameter estimation and v) model validation.
Cite
@article{arxiv.1901.10867,
title = {Uplift Regression: The R Package tools4uplift},
author = {Mouloud Belbahri and Alejandro Murua and Olivier Gandouet and Vahid Partovi Nia},
journal= {arXiv preprint arXiv:1901.10867},
year = {2021}
}