CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering

• URL: http://arxiv.org/abs/2607.03364v1
• Date: Fri, 03 Jul 2026 14:20:22 GMT
• Title: CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
• Abstract summary: CaSPECT defines similarity through the topology of a learned directed acyclic graph (DAG)<n>A novel emphOrientation Validation Score (OVS) combines PC bootstrap evidence with DirectLiNGAM to orient edges robustly.<n>Chung's directed Laplacian provides a spectral embedding in which individuals close together share the same causal propagation pathways.
• Score: 16.993547305381327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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