DualUplift: Two-model estimator

In tools4uplift: Tools for Uplift Modeling

Description

Fit the two-model uplift model estimator.

Usage

## S3 method for class 'formula'
DualUplift(formula, treat, data, ...)

## Default S3 method:
DualUplift(data, treat, outcome, predictors, ...)

Arguments

data, formula	a data frame containing the treatment, the outcome and the predictors or a formula describing the model to be fitted.
treat	name of a binary (numeric) vector representing the treatment assignment (coded as 0/1).
outcome	name of a binary response (numeric) vector (coded as 0/1).
predictors	a vector of names representing the explanatory variables to include in the model.
...	additional arguments (other than formula, family, and data) to be passed to glm function for each sub-model.

Value

model0	Fitted model for control group
model1	Fitted model for treatment group

Author(s)

Mouloud Belbahri

References

Hansotia, B., J., and Rukstales B. (2001) Direct marketing for multichannel retailers: Issues, challenges and solutions. Journal of Database Marketing and Customer Strategy Management, Vol. 9(3), 259-266.

Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>

Examples

library(tools4uplift)
data("SimUplift")

fit <- DualUplift(SimUplift, "treat", "y", predictors = colnames(SimUplift[, 3:12]))

print(fit)
summary(fit)

tools4uplift documentation built on Jan. 6, 2021, 5:09 p.m.