BinUplift: Univariate quantization

Description

Univariate optimal partitionning for Uplift Models. The algorithm quantizes a single variable into bins with significantly different observed uplift.

Usage

BinUplift(data, treat, outcome, x, n.split = 10, alpha = 0.05, 
          n.min = 30, ylim = NULL, ylab = "Uplift",
          title = "Binning Results", color = NULL)

Arguments

data

a data frame containing the treatment, the outcome and the predictor to quantize.

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).

name of the explanatory variable to quantize.

n.split

number of splits to test at each node. For continuous explanatory variables only (must be > 0).

alpha

significance level of the statistical test (must be between 0 and 1).

n.min

minimum number of observations per child node.

ylim

a range for the y axis.

ylab

a title for the y axis.

title

an overall title for the plot.

color

a color for the plot. If ommitted, the color will be set by default to a custom light blue.

Value

out.tree

Descriptive statistics for the different nodes of the tree

sas.code

SAS code generated for variable quantization

References

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")

binX1 <- BinUplift(data = SimUplift, treat = "treat", outcome = "y", x = "X1", 
                  alpha = 0.10, n.min = 3, title = "Binning for X1")

# }