  Conducting experiments to estimate total effects can be challenging due to
cost, ethical concerns, or practical limitations. As an alternative,
researchers often rely on causal graphs to determine if it is possible to
identify these effects from observational data. Identifying total effects in
fully specified non-temporal causal graphs has garnered considerable attention,
with Pearl's front-door criterion enabling the identification of total effects
in the presence of latent confounding even when no variable set is sufficient
for adjustment. However, specifying a complete causal graph is challenging in
many domains. Extending these identifiability results to partially specified
graphs is crucial, particularly in dynamic systems where causal relationships
evolve over time. This paper addresses the challenge of identifying total
effects using a specific and well-known partially specified graph in dynamic
systems called a summary causal graph, which does not specify the temporal lag
between causal relations and can contain cycles. In particular, this paper
presents sufficient graphical conditions for identifying total effects from
observational data, even in the presence of hidden confounding and when no
variable set is sufficient for adjustment, contributing to the ongoing effort
to understand and estimate causal effects from observational data using summary
causal graphs.
