tools4uplift (version 1.0.0)

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)

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).

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.

Value

out.tree

Descriptive statistics for the different nodes of the tree

References

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

See Also

predict.BinUplift

Examples

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

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

# }