I'm reading an article where the author has interacted two variables -- X and Y. The model contains variable X, variable Y, and the interaction of variables X and Y. However, I know from existing work that variable X is a predictor of variable Y. Is this problematic? If so, how?
If two of the independent variables are strongly correlated, this can lead to a (multi)collinearity problem. This can potentially make one or several of the variables appear non-significant, although they would be significant if the regression contained only either one of them. To check for collinearity, the VIF (variation inflation factors) is a good test - many empirical papers (depending on the field, of course) report the VIFs of their regressions. Regarding the interaction term, it will also be correlated with $X$ and $Y$, but I don't think this makes it any more problematic than the correlation between $X$ and $Y$ themselves.