[2510.14582] Local Causal Discovery for Statistically Efficient Causal Inference

Statistics > Machine Learning arXiv:2510.14582 (stat) [Submitted on 16 Oct 2025 (v1), last revised 31 Mar 2026 (this version, v2)] Title:Local Causal Discovery for Statistically Efficient Causal Inference Authors:Mátyás Schubert, Tom Claassen, Sara Magliacane View a PDF of the paper titled Local Causal Discovery for Statistically Efficient Causal Inference, by M\'aty\'as Schubert and 2 other authors View PDF Abstract:Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery methods focus on learning the whole causal graph and therefore enable the recovery of optimal adjustment sets, i.e., sets with the lowest asymptotic variance, but they quickly become computationally prohibitive as the number of variables grows. Local causal discovery methods offer a more scalable alternative by focusing on the local neighborhood of the target variables, but are restricted to statistically suboptimal adjustment sets. In this work, we propose Local Optimal Adjustments Discovery (LOAD), a sound and complete causal discovery approach that combines the computational efficiency of local methods with the statistical optimality of global methods. First, LOAD identifies the causal relation between the targets and tests if the causal effect is identifiable by using only local information. If it is identifiable, it finds the possible descendants of the treatment ...