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.
from Top Technology News -- ScienceDaily https://ift.tt/2HpUPTW
Tuesday, 5 June 2018
Beyond superstition to general causality: AI nutcracker for real-world problems
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