Artificial intelligence trained to analyze causation

The causes of real-world problems in economics and public health can be notoriously hard to determine. 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.

Artificial intelligence trained to analyze causation

The causes of real-world problems in economics and public health can be notoriously hard to determine. Often, multiple causes are suspected, but large datasets with time-sequenced data are not ...

Wed 6 Jun 18 from Phys.org

Beyond superstition to general causality: AI nutcracker for real-world problems

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. ...

Tue 5 Jun 18 from ScienceDaily

Beyond superstition to general causality: AI nutcracker for real-world problems, Tue 5 Jun 18 from Eurekalert

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