I have 5 variables for each countries of the world and I need to analyze their effect and interactions on an independent variable. Random Forest would be adequate for my scope as it deals with non-linear relationships and predicts variables importance. However, I'm wondering if spatial dependence may be an issue. I've never seen spatial dependence discussed in RF applications even if it has been widely used for spatial data.
It has no problem with spatial autocorrelation of your response or explanatory variables. It's a totally non-parametric technique. I have used it for the interpolation of structural diversity variables across my country based on in situ data from a regular grid and introducing the coordinates as covariables even produces better predictions. This is because Random Forest is based on a divide and conquer approach (classification and regression trees), meaning it separates your feature space into disjoint subsets where simpler models (by default a simple average in the case of regression) can produce good predictions. Introducing the coordinates as variables, in my case, exploits spatial autocorrelation as it makes sense that certain geographic subsets of the country behave homogeneously.