Real-world problems in economics and public health can be notoriously hard nuts to find causes for. Often, multiple causes are suspected but large datasets with time-sequenced data are not available. Previous models could not reliably analyze these challenges. Now researchers have tested the first Artificial Intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs.