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?
 A: 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. 
A: R package glmertree would be perfectly suited for this purpose. It allows for specifying a random effects structure, as well as specifying predictors that have to occur in the model, based on e.g., expert domain knowledge. This should be limited to a small set of predictors (1 or 2) though.
For further reference, see the package vignette (tutorial): https://cran.r-project.org/web/packages/glmertree/vignettes/glmertree.pdf.
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., & Kelderman, H. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behavior research methods, 50(5), 2016-2034. https://doi.org/10.3758/s13428-017-0971-x
Fokkema, M., Edbrooke-Childs, J., & Wolpert, M. (2021). Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data. Psychotherapy Research, 31(3), 329-341. https://doi.org/10.1080/10503307.2020.1785037
