  Understanding causal relations in dynamic systems is essential in
epidemiology. While causal inference methods have been extensively studied,
they often rely on fully specified causal graphs, which may not always be
available in complex dynamic systems. Partially specified causal graphs, such
as summary causal graphs (SCGs), provide a simplified representation of causal
relations, omitting temporal information and focusing on high-level causal
structures. This simplification introduces new challenges concerning the types
of queries of interest: macro queries, which involve relationships between
clusters represented as vertices in the graph, and micro queries, which pertain
to relationships between variables that are not directly visible through the
vertices of the graph. In this paper, we first clearly distinguish between
macro conditional independencies and micro conditional independencies and
between macro total effects and micro total effects. Then, we demonstrate the
soundness and completeness of the d-separation to identify macro conditional
independencies in SCGs. Furthermore, we establish that the do-calculus is sound
and complete for identifying macro total effects in SCGs. Finally, we give a
graphical characterization for the non-identifiability of macro total effects
in SCGs.
