  Consider the case where cause-effect relationships between variables can be
described as a directed acyclic graph and the corresponding linear structural
equation model. This paper provides graphical identifiability criteria for
total effects by using surrogate variables in the case where it is difficult to
observe a treatment/response variable. The results enable us to judge from
graph structure whether a total effect can be identified through the
observation of surrogate variables.
