General Causality Model Using Causal Independence and Causal Influence (VIDEO)
Caption
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. The model uses Causal Independence and Causal Influence to build a multi-nodal causal structure, using Directed Acyclic Graphs (DAGs). Researchers from the University of Johannesburg and National Institute of Technology Rourkela, India, tested the General Causality model on simulated real-world data sets.
Credit
Ms. Therese van Wyk, University of Johannesburg
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