{"ID":5935717,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03364","arxiv_id":"2607.03364","title":"CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering","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.","short_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 t...","url_abs":"https://arxiv.org/abs/2607.03364","url_pdf":"https://arxiv.org/pdf/2607.03364v1","authors":"[\"Arghya Pratihar\",\"Shinjon Chakraborty\",\"Swagatam Das\"]","published":"2026-07-03T14:20:22Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.LG\"]","methods":"[]","has_code":false}