Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion
This work addresses a crucial challenge for researchers in dynamic systems where specifying complete causal graphs is difficult, offering an extension of the front-door criterion to enable causal inference from observational data in such settings.
The paper tackled the problem of identifying total effects from observational data in partially specified summary causal graphs with cycles and latent confounding, presenting sufficient graphical conditions for identifiability even when no variable set is sufficient for adjustment.
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 whether these effects can be identified from observational data. Identifying total effects in fully specified causal graphs has received 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 cycles and latent confounding, and when no variable set is sufficient for adjustment.