Presumably the coupling of the variables is not binary (dependent or independent) but variable (degrees of coupling). Presumably these variables were more tightly coupled in the past than in the present. Presumably it's useful to understand precisely how coupled these variables are today because it would drive our approach to addressing these disparities. E.g., if the variables are loosely coupled then bias-reducing programs would have a marginal impact on the disparities and the better investment would be social welfare programs (and the inverse is true if the variables are tightly coupled).