Uplift Regression: The R Package tools4uplift

TL;DR

This paper introduces the R package tools4uplift, which provides statistical regression tools for uplift modeling, enabling better prediction of individual causal effects in treatment versus control groups.

Contribution

The paper presents a comprehensive R package that fills the gap by offering regression-based tools for uplift modeling, including quantization, visualization, feature selection, and validation.

Findings

Provides tools for statistical regression in uplift modeling

Enhances model validation and feature selection processes

Facilitates causal effect prediction in individual-level analysis

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.