Decision Trees and SHAP Values I've recently been using some (optimal) decision trees methods in R, such as 'evtree' and 'iai.'
Both of these provide really nice interpretable plots. And out of the 12 covariates I have in my model, the optimal tree (say for example, using 'evtree') is typically described by 3-4 covariates.
However, when I calculate my Shapley values for the evtree, it is unusual that many of remaining 8-9 covariates which are not in the optimal tree, often have a very high Shapley value.
On a conceptual basis is it possible for decision trees and Shapley's to depart/express inconsistency in terms of the information they provide?
Would be good to hear your thoughts

Shapley value calculations assume all the features are uncorrelated. Correlated features will split the Shapley values among themselves in unexpected way, usually depending on their scales.
The optimal trees probably selected one for each correlated groups to minimize unwanted variance from extra dimensions in the model.
If you run PCA on your features, you may find 3~4 dimensions to be optimal, for example.
