Incremental effect modeling by area index maximization

A computer program product for determining an incremental effect model, the computer program product comprising:

• a non-transitory computer-readable medium comprising:
• an executable portion for causing a computer to determine at least one model variable from a plurality of predictor variables, wherein the model variable is associated with a response variable or a performance variable and a decision variable for a population, wherein the decision variable defines treated observations and control observations in the population;
• an executable portion for causing a computer to determine a difference in the response variable between the treated observations and the control observations;
• an executable portion for causing a computer to determine a function describing the relationship between the difference and the model variable;
• an executable portion for causing a computer to rank order the treated and control observations in the population based on a score, which is a weighted sum of functions of some model variables;
• an executable portion for causing a computer to determine a cumulative incremental effect between the response value for the treated and control observations at increasing percentages of ranked observations with the lowest score values;
• an executable portion for causing a computer to determine an incremental effect area index based on the cumulative incremental effect as a function of the increasing percentages of ranked observations with the lowest score values;
• and
• an executable portion for causing a computer to iterate through the functions of the model variables until the incremental effect area index is maximized to determine the incremental effect model.

A computer-implemented method of determining an incremental effect model, the method comprising:

• determine a set of model variables from a plurality of predictor variables that are decision relevant and optionally time invariant, wherein the predictor variables are associated with a response variable or a performance variable and a decision variable for a population, wherein the decision variable defines treated observations and control observations in the population;
• determine a function describing the relationship between an incremental effect and the model variable, wherein the incremental effect is defined as the difference between performance of an individual under treatment and the performance of the individual under no treatment;
• rank order the treated and control observations in the population based on a score from low to high, wherein the score is a weighted sum of a set of functions created from the model variables;
• determine a cumulative incremental effect at increasing percentages of ranked observations with the lowest score values, based on the combined test and control data;
• determine an incremental effect area index based on the cumulative incremental effect as a function of the increasing percentages of ranked observations with the lowest score values;
• and
• iterate through the functions of the model variables for weight search until the incremental effect area index is maximized to determine an incremental effect model;
• rank ordering the model variables based on a significance value for a two-term interaction between the decision variable and a predictor variable;
• determining a function describing the relationship between the incremental effect and the model variable.