CaSPECT：通过有向谱聚类发现因果同质亚组
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
AI总结：
该研究提出CaSPECT因果谱聚类框架，基于学习到的DAG拓扑定义相似性，从观测数据中识别因果同质亚组，无需预先指定倾向得分模型即可校正混杂，提升因果亚组发现可靠性。
摘要：
我们提出CaSPECT，这是一种用于从观测数据中发现因果同质亚组的因果谱聚类框架。不同于在协变量空间中聚类，CaSPECT通过学习得到的有向无环图（DAG）的拓扑结构定义相似度：经自举稳定的PC算法恢复因果骨架；全新的方向验证得分（OVS）结合PC自举证据与DirectLiNGAM实现鲁棒的边定向；有向边通过经OLS或双重机器学习估计的、由后门准则识别的平均处理效应加权。Chung有向拉普拉斯算子提供谱嵌入，嵌入中邻近的个体共享相同的因果传播路径。我们证明了全流程的几乎必然一致性，并通过受控仿真研究以及在LaLonde CPS1、IHDP和401(k)数据集上验证了该方法，CaSPECT可在因果可比亚组中得到正向且统计显著的处理效应，无需预先指定倾向得分模型即可校正严重混杂。
英文摘要：
We propose \textbf{CaSPECT}, a causal spectral clustering framework for discovering causally homogeneous subgroups from observational data. Rather than clustering in covariate space, CaSPECT defines similarity through the topology of a learned directed acyclic graph (DAG); a bootstrap-stabilised PC algorithm recovers the causal skeleton; a novel \emph{Orientation Validation Score} (OVS) combines PC bootstrap evidence with DirectLiNGAM to orient edges robustly; directed edges are weighted by backdoor-identified average treatment effects estimated via OLS or double machine learning. Chung's directed Laplacian provides a spectral embedding in which individuals close together share the same causal propagation pathways. We establish almost-sure consistency of the full pipeline and validate the method through a controlled simulation study and on LaLonde CPS1, IHDP, and 401(k) datasets, where CaSPECT recovers a positive and statistically significant treatment effect within the causally comparable subpopulation and corrects for severe confounding without requiring a pre-specified propensity score model.
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