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.

使用摘要因果图从观测数据中确定总效应的充分图形条件，即使存在隐藏的混杂和没有足够的变量集进行调整，也有助于从观测数据中理解和估计因果效应的持续努力。

潜在混淆变量的总效应在总结性因果图中的可识别性：基于前门准则的扩展

Toward identifiability of total effects in summary causal graphs with  latent confounders: an extension of the front-door criterion

Assume that cause-effect relationships between variables can be described as
a directed acyclic graph and the corresponding linear structural equation
model.We consider the identification problem of total effects in the presence
of latent variables and selection bias between a treatment variable and a
response variable. Pearl and his colleagues provided the back door criterion,
the front door criterion (Pearl, 2000) and the conditional instrumental
variable method (Brito and Pearl, 2002) as identifiability criteria for total
effects in the presence of latent variables, but not in the presence of
selection bias. In order to solve this problem, we propose new graphical
identifiability criteria for total effects based on the identifiable factor
models. The results of this paper are useful to identify total effects in
observational studies and provide a new viewpoint to the identification
conditions of factor models.

本文提出基于可识别因子模型的新的图形可识别性标准，以解决存在隐变量和选择偏差情况下，总效应识别问题。该标准可用于识别观察研究中的总效应，并为因子模型的识别条件提供了新的视角。