Permuting one feature barely changes validation performance, but that feature is strongly correlated with another predictor. What should be concluded?

1. Low permutation impact may reflect shared information rather than proof the feature is useless
2. The feature is proven to have no relationship to the target
3. The model must have been trained without the feature
4. Permutation importance proves causality