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.