  Imputation procedures in biomedical fields have turned into statistical
practice, since further analyses can be conducted ignoring the former presence
of missing values. In particular, non-parametric imputation schemes like the
random forest or a combination with the stochastic gradient boosting have shown
favorable imputation performance compared to the more traditionally used MICE
procedure. However, their effect on valid statistical inference has not been
analyzed so far. This paper closes this gap by investigating their validity for
inferring mean differences in incompletely observed pairs while opposing them
to a recent approach that only works with the given observations at hand. Our
findings indicate that machine learning schemes for (multiply) imputing missing
values may inflate type-I-error or result in comparably low power in small to
moderate matched pairs, even after modifying the test statistics using Rubin's
multiple imputation rule. In addition to an extensive simulation study, an
illustrative data example from a breast cancer gene study has been considered.
