  In aggregated variables the impact of interventions is typically ill-defined
because different micro-realizations of the same macro-intervention can result
in different changes of downstream macro-variables. We show that this
ill-definedness of causality on aggregated variables can turn unconfounded
causal relations into confounded ones and vice versa, depending on the
respective micro-realization. We argue that it is practically infeasible to
only use aggregated causal systems when we are free from this ill-definedness.
Instead, we need to accept that macro causal relations are typically defined
only with reference to the micro states. On the positive side, we show that
cause-effect relations can be aggregated when the macro interventions are such
that the distribution of micro states is the same as in the observational
distribution; we term this natural macro interventions. We also discuss
generalizations of this observation.
