[2603.02204] Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions

Computer Science > Machine Learning arXiv:2603.02204 (cs) [Submitted on 2 Mar 2026] Title:Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions Authors:Amir Asiaee, Kavey Aryan, James P. Long View a PDF of the paper titled Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions, by Amir Asiaee and 2 other authors View PDF HTML (experimental) Abstract:Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be learned from data. Our contributions are: (i) a contamination-robust conformal coverage theorem that quantifies how misclassification of "unaffected" calibration examples degrades coverage via an explicit function $g(\delta,n)$ of the contamination fraction and calibration set size, providing a finite-sample lower bound that holds for arbitrary contaminating distributions; (ii) a task-driven partial causal learning formulation that estimates only the binary descendant indicators $Z_{a,i}=\mathbf{1}\{i\in\mathrm{desc}(a)\}$ needed for selective calibration, rathe...