Abstract: One or more computer processors responsive to receiving an input, a trained model, a trained model outcome, a local outlier factor (LOF) threshold, and a maximum number of live polytopes to search over, generating an optimization problem to determine an output that is closest to the input with respect to a distance measure. The one or more computer processors transform a LOF constraint into a set of linear mixed integer constraints and a set of quadratic mixed integer constraints, utilizing the distance measure. The one or more computer processors decompose an input space into a plurality of polytopes based on a geometry associated with the trained model. The one or more computer processors generate a counterfactual based on the plurality of polytopes.

Abstract: A method, system, and computer program product are configured to: train an artificial neural network (ANN) model using a dataset comprising observational data including treatment data, outcome data, and covariate data, wherein the ANN model includes rectified linear unit (ReLU) activation functions and K number of output nodes corresponding to K number of treatment options; and create a prescriptive tree based on the ANN model, wherein each leaf node of the prescriptive tree corresponds to one of the treatment options, and wherein the prescriptive tree is configured to indicate one of the treatment options for a particular set of features of the covariate data.