General Causality: Identifying the Multiple Causes of Real-World Problems (VIDEO)
Caption
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.
Credit
Ms. Therese van Wyk, University of Johannesburg
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