Abstract: Certain aspects of the present disclosure provide techniques for determining sensitivities of result variables to driver variables of a system. Various systems can be represented by a directed acyclic graph representation using nodes and edges to represent entities and relationships. A residual at the nodes can be determined to isolate the effect of a particular node on downstream nodes from the effect of upstream nodes on the particular node. High sensitivities to features associated with the system can be determined by weights that are components of a hyperparameter value resulting from optimizing an algorithmic sum of a loss function and the hyperparameter. Sensitivities to features can be used to present or prevent intervention using sensitive features.

Abstract: Systems and methods for identifying one or more master tables of a datalake are described. A system may obtain a plurality of computer readable tables of a datalake (with each computer readable table including one or more features). The system may also group the plurality of computer readable tables into a plurality of groups based on a number of features of each computer readable table of the plurality of computer readable tables. The system may further generate, for each of one or more groups of the plurality of groups, one or more neighborhoods based on a similarity of features between computer readable tables of the group. The system may also identify, for each neighborhood, one or more master tables from the one or more computer readable tables of the group. The system may further provide an indication of one or more master tables identified in the datalake.