Machine learning techniques for generating impact measures using nonlinear causal inference and predictive risk scores

Abstract

Various embodiments of the present disclosure provide techniques for improving causal inference modeling with respect to predictive labels in a complex predictive domain. The techniques of the present disclosure may include generating a positive cohort and a negative cohort of data objects from a dataset based on an associated with a predictive label, generating a positive cohort impact measure for the positive cohort and one or more negative cohort impact measures for the negative cohort, and generating an object impact measure for a particular data object of the negative cohort based on the positive cohort impact measure and at least one of the negative cohort impact measures.