Much of scientific data is collected as randomized experiments
intervening on some and observing other variables of interest. Quite
often, a given phenomenon is investigated in several studies, and
different sets of variables are involved in each study. In this
article we consider the problem of integrating such knowledge,
inferring as much as possible concerning the underlying causal
structure with respect to the union of observed variables from
such experimental or passive observational overlapping data
sets. We do not assume acyclicity or joint causal sufficiency of the
underlying data generating model, but we do restrict the causal
relationships to be linear and use only second order statistics of
the data. We derive conditions for full model identifiability in the
most generic case, and provide novel techniques for incorporating an
assumption of faithfulness to aid in inference. In each case we seek
to establish what is and what is not determined by the data at hand.
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