Causal Decision Making with non-Causal Models

In a previous post I noted: " ...correlations or 'flags' from big data might not 'identify' causal effects, but they are useful for prediction and might point us in directions where we can more rigorously investigate causal relationships" Recently on LinkedIn I discussed situations where we have to be careful about taking action on specific features in a correlational model, for instance changing product attributes or designing an intervention based on interpretations of SHAP values from non-cau