发表机构
University of Jyvaskyla（于韦斯屈莱大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究因果图中聚类操作对因果效应识别的影响，提出基于c-分量的条件，定义并刻画了一类识别不变的聚类操作，并展示了其实际应用。
AI 中文摘要
在因果图中对变量进行聚类可以减小图的规模并简化因果推断。然而，任意聚类可能会改变变量间关键的因果关系，从而导致错误的结论。虽然在一定温和条件下，聚类图中的因果效应可识别性蕴含原始图中的可识别性，但在没有进一步假设的情况下，聚类图中的不可识别性并不蕴含原始图中的不可识别性。当可识别性和不可识别性均被保留时，该聚类操作被称为识别不变。我们基于原始图的c-分量的相关条件，提出了一类广泛的识别不变的聚类操作。最后，我们展示了这些结果在实际场景中的应用。
英文摘要
Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variables and lead to erroneous conclusions. While the identifiability of a causal effect in the clustered graph implies the identifiability in the original graph under mild conditions, nonidentifiability in clustered graph does not imply nonidentifiability in the original graph without further assumptions. When both identifiability and nonidentifiability are preserved, the clustering operation is called identification invariant. We present a broad class of clustering operations that are identification invariant based on conditions related to the c-components of the original graph. Finally, we demonstrate use of the results in practical settings.
