EPS
← All batches·2605.15639

Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs

topic: current_projecttop score: 100released: 2026-05-18first surfaced: 2026-05-18arXivPDFlinked_to_results2026-05-18

Authors: Hyunwoong Chang, Fariha Taskin

arXiv · PDF

Summary

arXiv:2605. 15639v1 Announce Type: cross Abstract: We propose a joint order-based scoring framework for causal structure learning of directed acyclic graph (DAG) models under heterogeneous data settings.

Relevance

Read next because Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs overlaps with clean result "LoRA persona trained on alone emits at 23.5% when a co-trained partner learns ..., vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, rect, under, rate, model. Source: arxiv stat.ML (Machine Learning).

Abstract

arXiv:2605.15639v1 Announce Type: cross Abstract: We propose a joint order-based scoring framework for causal structure learning of directed acyclic graph (DAG) models under heterogeneous data settings. We show that leveraging heterogeneity improves the accuracy of causal ordering estimation. In the most favorable case, the causal ordering is identifiable up to two permutations. Building on this framework, we propose an order-based Bayesian method for Gaussian DAG models and establish its theoretical properties in the high-dimensional regime. For posterior inference over the space of orderings, we introduce a random-to-random (R2R) proposal neighborhood for the Metropolis-Hastings algorithm, which is theoretically motivated and exhibits efficient mixing behavior. Simulation studies confirm the strong empirical performance of the proposed method, and an application to single-nucleus RNA sequencing data from major depressive disorder demonstrates practical utility.