# Is there a method for constructing decision trees that takes account of structured/hierarchical/multilevel predictors?

Is there a method for constructing decision trees that takes account of structured/hierarchical/multilevel predictors, that would allow me to impose domain knowledge or constraints on interactions for example?

If you have metric responses, there is RE-EM tree by Sela and Simonoff (Machine Learning, 86, 169-207). The R package is called REEMtree. It is intended for panel data with random effects, but you should be able to use it for other hierarchically nested/multilevel data as well.
If you are fine with including the domain expertise in a fixed effect model, you can also use model-based recursive partioning with the party::mob function.