AI 中文总结
该研究提出基于图的框架定义有序方差分量的因果对应物，建立其从观测数据的可识别性，提出相关估计方法并通过模拟研究验证性能，拓展了因果方差分解的应用场景。
AI 中文摘要
全方差定律的递归应用可将结果的边际方差分解为归因于解释变量的分量和残差分量。所得分解依赖于所选的条件顺序，其分量通常不具有因果解释。我们开发了一种基于图的框架，用于定义有序方差分量的因果对应物，并建立其从观测数据中的可识别性。在拓扑排序下，可针对完整因果图逐分量评估可识别性，无需分解中包含的变量构成因果充分系统。我们还考虑了偏离拓扑排序的科学动机情形，即对选定的中间变量进行条件化以获得受控效应解释，该情形受医疗保健提供差异的背景启发。我们将所得估计量与机器学习中的因果归因和变量重要性方法相关联，提出基于模型的插件估计量以及用于不确定性量化的近似贝叶斯程序。一项模拟研究检验了有限样本性能、对结果模型误设的敏感性以及灵活机器学习估计的表现。
英文摘要
Recursive application of the law of total variance decomposes the marginal variance of an outcome into components attributed to explanatory variables and a residual component. The resulting decomposition depends on the chosen conditioning order, and its components do not in general have causal interpretations. We develop a graph-based framework for defining causal counterparts of ordered variance components and establishing their identification from observed data. Under topological orderings, identification can be assessed component by component against the full causal graph, without requiring the variables included in the decomposition to form a causally sufficient system. We also consider scientifically motivated departures from topological orderings, in which selected intermediate variables are conditioned on to obtain controlled-effect interpretations, motivated by the context of disparities in healthcare delivery. We relate the resulting estimands to causal attribution and variable-importance approaches in machine learning, and propose model-based plug-in estimators together with an approximate Bayesian procedure for uncertainty quantification. A simulation study examines finite-sample performance and sensitivity to outcome-model misspecification and flexible machine-learning estimation.
