  In many scientific contexts, different investigators experiment with or
observe different variables with data from a domain in which the distinct
variable sets might well be related. This sort of fragmentation sometimes
occurs in molecular biology, whether in studies of RNA expression or studies of
protein interaction, and it is common in the social sciences. Models are built
on the diverse data sets, but combining them can provide a more unified account
of the causal processes in the domain. On the other hand, this problem is made
challenging by the fact that a variable in one data set may influence variables
in another although neither data set contains all of the variables involved.
Several authors have proposed using conditional independence properties of
fragmentary (marginal) data collections to form unified causal explanations
when it is assumed that the data have a common causal explanation but cannot be
merged to form a unified dataset. These methods typically return a large number
of alternative causal models. The first part of the thesis shows that marginal
datasets contain extra information that can be used to reduce the number of
possible models, in some cases yielding a unique model.
