BinUplift: Univariate quantization
In tools4uplift: Tools for Uplift Modeling

Description Usage Arguments Value Author(s) References See Also Examples

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

1	BinUplift(data, treat, outcome, x, n.split = 10, alpha = 0.05, n.min = 30)

`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).
`x`	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). If n.split = 10, the test will be executed at each decile of the variable.
`alpha`	significance level of the statistical test (must be between 0 and 1).
`n.min`	minimum number of observations per child node.

out.tree

Descriptive statistics for the different nodes of the tree

Mouloud Belbahri

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

predict.BinUplift

library(tools4uplift)
data("SimUplift")

binX1 <- BinUplift(data = SimUplift, treat = "treat", outcome = "y", x = "X1", 
                  n.split = 100, alpha = 0.01, n.min = 30)