Abstract
The development paper investigates claims made for Causal AI methods - specifically that they can identify underlying causal relationships and and not confuse correlation with causation. We put this to the test by creating a dataset containing both causal and correaltional relationships and applying a number of Causal AI algorithms. All of them identified the correlational relationship as causal. We highlight that caution, and subject knowledge, are needed when interpreting the output produced.