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Much of scientific data is collected as randomized experiments intervening onsome and observing other variables of interest. Quite often, a given phenomenonis investigated in several studies, and different sets of variables areinvolved in each study. In this article we consider the problem of integratingsuch knowledge, inferring as much as possible concerning the underlying causalstructure with respect to the union of observed variables from suchexperimental or passive observational overlapping data sets. We do not assumeacyclicity or joint causal sufficiency of the underlying data generating model,but we do restrict the causal relationships to be linear and use only secondorder statistics of the data. We derive conditions for full modelidentifiability in the most generic case, and provide novel techniques forincorporating an assumption of faithfulness to aid in inference. In each casewe seek to establish what is and what is not determined by the data at hand.

Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables