Correlation by itself without known connecting mechanisms or relationships does not imply causation.
The bayesian approach would suggest that we assign a causation-credence to every correlation we observe. Of course detecting confounders is very important since it provides you with updates. However, a correlation without known connecting mechanisms does imply causation. In particular it does it probabilistically. A bayesian updater would prefer talking about credences in causation which can be shifted up and downwards. It would be a (sometimes dangerous) simplification to in our map deal with discrete values like “just correlation” and “real causation”. However, such a simplification may be of use as a heuristic in everyday life, still I’d suggest not to overgeneralize it.
The bayesian approach would suggest that we assign a causation-credence to every correlation we observe. Of course detecting confounders is very important since it provides you with updates. However, a correlation without known connecting mechanisms does imply causation. In particular it does it probabilistically. A bayesian updater would prefer talking about credences in causation which can be shifted up and downwards. It would be a (sometimes dangerous) simplification to in our map deal with discrete values like “just correlation” and “real causation”. However, such a simplification may be of use as a heuristic in everyday life, still I’d suggest not to overgeneralize it.