BinUplift function

Univariate quantization

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

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.

Returns

• 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

Author(s)

Mouloud Belbahri

See Also

predict.BinUplift

Examples

library(tools4uplift)
data("SimUplift")

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