In machine learning - notably ensemble methods such as random forest, gradient boosting, extreme gradient boosting etc - can we say that the effect obtained for one predictor is ADJUSTED for all other predictors?
Details: Let's say we have a classicial linear regression model, like so:
Y = X1 + X2 + Xn ...
In this case, we could say that the coefficient for X1 is adjusted for the effect of X2, Xn etc. Hence, we obtain adjusted estimates of the effect of each predictor.
But is that also true for random forest, gradient boosting, extreme gradient boosting etc? Those tree-based models provide several important parameters (variable importance, partial dependence plots etc). But is the effect of each predictor (for example, the association seen in partial dependence plots) adjusted for all other predictors? Can we really say that?