Is it Valid to Aggregate SHAP values to Sets of of Features?
From Lundberg, package author: "The short answer is yes, you can add up SHAP values across the columns to get the importance of a whole group of features (just make sure you don't take the absolute value like we do when going across rows for global feature importance).
The long answer is that when Shapley values "fairly" allocate credit for interaction effects between features, they assume each feature in an interaction effect should get equal credit for the interaction. This means that for high order interaction terms you might get slightly different results when running Shapley values before (and summing) vs. after grouping features (since the new group just gets one chunk of the interaction pie so to speak, as opposed to multiple chunks when it was several features). These differences are typically small though so I wouldn't sweat it much since both ways are reasonable."
SHAP values seem to be additive and e.g. the overall feature importance plot simply adds the absolute SHAP values per feature and compares them.  This allows us to use SHAP for global importance aswell as local importance. We could also get feature importance for a particular subset of data records the same way.  
By the same token, is it valid to get aggregate SHAP values for sets of variables? e.g. "Height", "Weight" and "Eye Colour" into "HumanDescription" or "Temperature", "Humidity" and "Air-Pressure" into "Weather"and rank them accordingly.
Theoretically, I can't see why not but would appreciate feedback on this in case of any gotchas.