A fitted model can reveal predictive relationships without explaining what would happen under an intervention. You will inspect logistic coefficients, shuffle inputs on held-out rows and identify redundant columns before describing importance. Standardization changes the unit of a coefficient, and correlated inputs can share the same information. A feature with a small permutation drop may still belong to a useful group of predictors. Negative estimates can appear because the evaluation sample and the shuffles are finite. Preserve that uncertainty rather than clipping it out of the report. None of these quantities alone proves that changing a feature would change the outcome.