tools4uplift (version 1.0.0)

DualUplift: Two-model estimator

Description

Fit the two-model uplift model estimator.

Usage

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

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

# NOT RUN {
library(tools4uplift)
data("SimUplift")

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

print(fit)
summary(fit)
# }