提出在隐变量干扰下识别因果图中微观直接效应的方法。
Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs
- 基于摘要因果图，给出控制型与自然型微观直接效应的可识别条件。
- 在存在隐性混杂时，仍能通过特定条件识别平均直接效应。
- 适用于流行病学等非线性复杂系统，适合做因果推断研究者参考。
本文研究摘要因果图中平均控制型微观直接效应与平均自然型微观直接效应的可识别性。摘要因果图是全因果图的抽象，常用于包含循环和缺失时间信息的动态系统，使因果推断复杂化。与传统线性设定中直接效应较易识别不同，非参数直接效应对处理现实世界复杂性（如遗传、环境与行为因素间的非线性关系）至关重要，但更难定义与识别。本文给出了在存在隐性混杂时，平均控制型微直接效应与平均自然型微直接效应可识别的充分条件。此外，还证明在无隐性混杂且仅关注调整可识别性的场景下，所给条件亦为必要。
原文摘要 · Abstract (English)
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.
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