Let there be the random variables $X$ and $Y$. We have a sample of $X$'s and Y's together. To most accurately estimate the density $f_X$, would we...
1) Ignore the Y's and estimate $f_X$ only from the $X$'s
2) Estimate $f_{X, Y}$ and then find the marginal $f_X$ by integrating $f_{X, Y}$ (let us assume here that numeric integration is both fast and precise)
I suppose the crux of the question is this: Do we learn more about the marginal distribution of $X$ from observing samples of $X$ and $Y$ versus $X$ alone?
I think analytically both approaches are equivalent: all information you can gain from $X$ lies within its probability distribution $f_X$. Using $Y$ as well as $X$ only is convenient if you are interested in the combined probability distribution $f_{X,Y}$.
If we are talking about numerical efficiency, it seems estimating $f_X$ from $X$ avoids computations and thus is faster and more accurate (however, the actual difference might be negligible).