I am quite new to machine learning, CART-techniques and the like, and I hope my naivete isn't too obvious.

**How does Random Forest handle multi-level/hierarchical data structures (for example when cross-level interaction is of interest)?**

That is, data sets with units of analysis at several hierarchical levels (*e.g.*, students nested within schools, with data about both the students and the schools).

Just as an example, consider a multi-level data set with individuals on the first level (*e.g.*, with data on voting behavior, demographics etc.) nested within countries at the second level (with country-level data; *e.g.*, population):

> 
> `ID voted age female country population`  
> 
> `1 1 19 1 1 53.01`  
> 
> `2 1 23 0 1 53.01`  
> 
> `3 0 43 1 1 53.01`  
> 
> `4 1 27 1 1 53.01`  
> 
> `5 0 67 0 1 53.01`  
> 
> `6 1 34 1 2 47.54`  
> 
> `7 0 54 1 2 47.54`  
> 
> `8 0 22 1 2 47.54`  
> 
> `9 0 78 0 2 47.54`  
> 
> `10 1 52 0 2 47.54`  
> 
> 
> 
> 

Lets say that `voted` is the response/dependent variable and the others are predictor/independent variables. In these types of cases, margins and marginal effects of a variable (partial dependence) for some higher-level variable (*e.g.*, `population`) for different individual-level variables, etc., could be very interesting. In a case similar to this, `glm` is of course more appropriate -- but when there are many variables, interactions and/or missing values, and/or very large-scale datasets etc., `glm` is not so reliable.

Subquestions: *Can Random Forest explicitly handle this type of data structure in some way? If used regardless, what kind of bias does it introduce? If Random Forest is not appropriate, is there any other ensemble-type method that is?*

(Question [Random forest on grouped data](https://stats.stackexchange.com/questions/19803/random-forest-on-grouped-data) is perhaps similar, but doesn't really answer this.)