QiniTable: Performance of an uplift estimator

Description

Table of performance of an uplift model. This table is used in order to vizualise the performance of an uplift model and to compute the qini coefficient.

Usage

QiniTable(data, treat, outcome, prediction, nb.group = 10)

Arguments

data

a data frame containing the response, the treatment and predicted uplift.

treat

a binary (numeric) vector representing the treatment assignment (coded as 0/1).

outcome

a binary response (numeric) vector (coded as 0/1).

prediction

a predicted uplift (numeric) vector to sort the observations from highest to lowest uplift.

nb.group

number of groups of equal observations in which to partition the data set to show results.

Value

a table with descriptive statistics related to an uplift model estimator.

References

Radcliffe, N. (2007). Using control groups to target on predicted lift: Building and assessing uplift models. Direct Marketing Analytics Journal, An Annual Publication from the Direct Marketing Association Analytics Council, pages 14-21.

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

Examples

Run this code

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

square1 <- SquareUplift(SimUplift, "X1", "X2", "treat", "y")

#performance of the heat map uplift estimation on the training dataset
perf <- QiniTable(data = square1, treat = "treat", 
                  outcome = "y", prediction = "Uplift_X1_X2")
                  
perf
# }