In this paper, we investigate the identifiability of average controlled direct effects and average natural direct effects in causal systems represented by summary causal graphs, which are abstractions of full causal graphs, often used in dynamic systems where cycles and omitted temporal information complicate causal inference. Unlike in the traditional linear setting, where direct effects are typically easier to identify and estimate, non-parametric direct effects, which are crucial for handling real-world complexities, particularly in epidemiological contexts where relationships between variables (e.g, genetic, environmental, and behavioral factors) are often non-linear, are much harder to define and identify. In particular, we give sufficient conditions for identifying average controlled micro direct effect and average natural micro direct effect from summary causal graphs in the presence of hidden confounding. Furthermore, we show that the conditions given for the average controlled micro direct effect become also necessary in the setting where there is no hidden confounding and where we are only interested in identifiability by adjustment.

翻译：本文研究了在摘要因果图中平均受控直接效应与平均自然直接效应的可识别性问题。摘要因果图是全因果图的抽象表示，常用于动态系统中，其中循环与省略的时间信息使因果推断复杂化。与传统线性设定（其中直接效应通常较易识别与估计）不同，非参数直接效应对于处理现实世界复杂性（尤其在流行病学背景下，变量间关系——如遗传、环境与行为因素——常呈非线性）至关重要，但其定义与识别则困难得多。具体而言，我们给出了在存在隐藏混杂因素时，从摘要因果图中识别平均受控微观直接效应与平均自然微观直接效应的充分条件。此外，我们证明在无隐藏混杂且仅关注通过调整实现可识别性的设定下，针对平均受控微观直接效应给出的条件同样成为必要条件。