Real-world problems in economics and public health can be very hard to analyze. 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, based on Directed Acyclic Graphs (DAGs).
Professor Tshilidzi Marwala, a researcher in Artificial Intelligence and Economics; and Dr. Pramod Kumar Parida from the University of Johannesburg tell us more.