  The investigation of the question "which treatment has a causal effect on a
target variable?" is of particular relevance in a large number of scientific
disciplines. This challenging task becomes even more difficult if not all
treatment variables were or even cannot be observed jointly with the target
variable. Another similarly important and challenging task is to quantify the
causal influence of a treatment on a target in the presence of confounders. In
this paper, we discuss how causal knowledge can be obtained without having
observed all variables jointly, but by merging the statistical information from
different datasets. We show how the maximum entropy principle can be used to
identify edges among random variables when assuming causal sufficiency and an
extended version of faithfulness, and when only subsets of the variables have
been observed jointly.
