  In view of the paradigm shift that makes science ever more data-driven, in
this paper we consider deterministic scientific hypotheses as uncertain data.
In the form of mathematical equations, hypotheses symmetrically relate aspects
of the studied phenomena. For computing predictions, however, deterministic
hypotheses are used asymmetrically as functions. We refer to Simon's notion of
structural equations in order to extract the (so-called) causal ordering
embedded in a hypothesis. Then we encode it into a set of functional
dependencies (fd's) that is basic input to a design-theoretic method for the
synthesis of U-relational databases (DB's). The causal ordering captured from a
formally-specified system of mathematical equations into fd's determines not
only the constraints (structure), but also the correlations (uncertainty
chaining) hidden in the hypothesis predictive data. We show how to process it
effectively through original algorithms for encoding and reasoning on the given
hypotheses as constraints and correlations into U-relational DB's. The method
is applicable to both quantitative and qualitative hypotheses and has underwent
initial tests in a realistic use case from computational science.
