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.

Abstract: Various embodiments of the present disclosure provide a optimized resource allocation using multi-level regression modeling that improves the functionality of a computer in various aspects. The techniques comprise synthesizing (i) a prediction stratification data structure that assigns a probability classification of a plurality of defined probability classifications for a requested class to an entity identifier of a recorded entity cohort with a (ii) a plurality of entity feature vectors that comprise an entity feature vector for the entity identifier of the recorded entity cohort to generate a multi-level regression model for the requested class.

Abstract: Systems and methods are disclosed for enhancing data. One or more processors may receive a data object associated with a user that includes a first parameter initially set to a first value. One or more processors may determine, based on a comparison of parameters of the data object with corresponding parameters of data objects associated with other users, that one or more of (1) a second value should override the first value or (2) a second parameter should be added into the data object. One or more processors may generate an augmented data object by modifying the data object to include the second value or the second parameter based on the determining. One or more processors may store or delete information about the user in memory based on the augmented data object.

Abstract: Systems and methods are disclosed for systems and methods for optimizing a sample size of an investigation.

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.

Abstract: Systems and methods are disclosed for allocating system resources. One or more processors may receive a plurality of system data sets and a plurality of user data sets. The one or more processors may determine one or more target systems by applying one or more filters to the plurality of system data sets and one or more target users associated. One or more processors may determine one or more target user data sets associated with each of the one or more target users. One or more processors may apply a machine-learning model to the one or more target user data sets to generate a user-level score. One or more processors may generate a system-level score for each of the one or more target systems associated with the one or more target users. One or more processors may initiate performance of one or more actions.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by generating an optimization function for generating an optimal amount of resources to allocate to data objects in each of one or more data object cohorts based on nonlinear causal effect predictions and generating an optimal parameter occurrence set based on the determined optimal amount of resource. Nonlinear causal effects of selected amounts of type-varied resources assigned to specific data objects are predicted on an outcome of interest associated with the data objects.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by determining an optimal amount of resources to allocate to resource-receiving entities in each of one or more resource-receiving entity cohorts based on non-linear causal effect predictions, and determining an optimum operation configuration based on the determined optimal amount of resource. Non-linear causal effect of selected amounts of resources assigned to specific resource-receiving entities are predicted on an outcome of interest associated with the resource-receiving entities.