  Understanding the behavior of a black-box model with probabilistic inputs can
be based on the decomposition of a parameter of interest (e.g., its variance)
into contributions attributed to each coalition of inputs (i.e., subsets of
inputs). In this paper, we produce conditions for obtaining unambiguous and
interpretable decompositions of very general parameters of interest. This
allows to recover known decompositions, holding under weaker assumptions than
stated in the literature.
