  The aim of this paper is to provide a new method for learning the
relationships between data that have been obtained independently. Unlike
existing methods like matching, the proposed technique does not require any
contextual information, provided that the dependency between the variables of
interest is monotone. It can therefore be easily combined with matching in
order to exploit the advantages of both methods. This technique can be
described as a mix between quantile matching, and deconvolution. We provide for
it a theoretical and an empirical validation.
