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 can not be observed jointly with the target variable. 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.