PerformanceUplift: 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

PerformanceUplift(data, treat, outcome, prediction, nb.group = 10, 
                  equal.intervals = TRUE, rank.precision = 2)

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

if equal.intervals is set to true, the number of groups of equal observations in which to partition the data set to show results.

equal.intervals

flag for using equal intervals (with equal number of observations) or the true ranking quantiles which result in an unequal number of observations in each group.

rank.precision

precision for the ranking quantiles. Must be 1 or 2. If 1, the ranking quantiles will be rounded to the first decimal. If 2, to the second decimal.

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")

model1 <- BinUplift2d(SimUplift, "X1", "X2", "treat", "y")
perf1 <- PerformanceUplift(data = model1, treat = "treat", 
                  outcome = "y", prediction = "Uplift_X1_X2", 
                  equal.intervals = TRUE, nb.group = 3)
                  
                  
print(perf1)
# }