Use the Uplift platform to model the incremental impact of an action, or treatment, on individuals. An uplift model helps identify groups of individuals who are most likely to respond to the action. Identification of these groups leads to efficient and targeted decisions that optimize resource allocation and impact on the individual. (See Radcliffe and Surry, 2011.)
The criterion used by the Uplift platform in defining splits is the significance of the test for interaction over all possible splits. However, predictor selection based solely on p-values introduces bias favoring predictors with many levels. For this reason, JMP adjusts p-values to account for the number of levels. (See the paper “Monte Carlo Calibration of Distributions of Partition Statistics” on the JMP website.) The splits in the Uplift platform are determined by maximizing the adjusted p-values for t tests of the interaction effects. The logworth for each adjusted p-value, namely -log10(adj p-value), is reported.