The paper introduces DECO, a non‑parametric hybrid framework that uses directional evidence from observational data to prune the search space of parent‑set configurations before performing exact DAG optimization. By eliminating empirically unsupported parent configurations, DECO achieves an exponential reduction in the admissible configuration space while maintaining flexibility for plausible edge orientations. Experiments on benchmark Bayesian networks and synthetic DAGs show substantial search‑space reduction and competitive structure‑recovery performance, with favorable structural Hamming distances across many settings.
By Upala Junaida Islam, Abdelmonem Elrefaey, Rong Pan
arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.
By Shrenik Zinage
The article discusses recent advances in learning directed acyclic graphs (DAGs) from observational data, highlighting the challenges of enforcing acyclicity and dealing with latent confounding or heterogeneous noise. It reviews the evolution from combinatorial search methods to continuous, score‑based approaches that use smooth acyclicity constraints, and introduces concomitant DAG estimation techniques that jointly infer sparse causal structure and exogenous noise levels to improve robustness under heteroscedasticity and distribution shifts. The tutorial also outlines future research directions such as online, nonlinear, and neural causal discovery.
By Gonzalo Mateos, Samuel Rey, Hamed Ajorlou, Mariano Tepper
arXiv:2609.30643v1 Announce Type: new
Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wit...
By Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni
CATune is a Bayesian optimization framework that incorporates deterministic inter‑knob ordering constraints from DBMS documentation into the search space. By performing optimization within a constraint‑consistent subspace and using a topology‑aware sampling strategy, it avoids costly post‑hoc constraint handling. Experiments on PostgreSQL and MySQL with TPC‑C and TPC‑H workloads show that CATune achieves up to 12.5× faster convergence to the baseline optimum and improves throughput by up to 63.37%.
By Fangping Lan, Qi Zhang, Eduard Dragut
arXiv:2610. 02785v1 Announce Type: cross Abstract: High-dimensional Gaussian DAG learning faces a statistical-computational gap: methods with sharp sample complexity rely on computationally expensive subset search and a supplied indegree bound, whereas polynomial-time alternatives have less favorable sample complexity.
By Donguk Shin, Byeongguk Kang, Inseol Lee, Gunwoong Park
The paper introduces COARSE, a score‑based method for learning cluster directed acyclic graphs (DAGs) from interventional data. COARSE retains the two‑phase structure of prior constraint‑based approaches—first identifying clusters, then edges—but replaces the edge‑learning phase with a score‑based local search under a cluster‑level BIC score, assuming linear Gaussian relationships. The authors prove polynomial‑time complexity and consistency (given correct intervention targets) and demonstrate that COARSE achieves comparable edge recovery to state‑of‑the‑art methods while being up to two orders of magnitude faster, even on dense graphs with hundreds of nodes.
By Gaetano Tedesco (University of Amsterdam), Alex Markham (University of Copenhagen)
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
By Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat, Harsh Vardhan Singh Chauhan, Niranjan Pedanekar
arXiv:2603. 04852v2 Announce Type: replace Abstract: Multi-step theorem prediction is a central challenge in geometry problem solving.
By Junbo Zhao, Ting Zhang, Can Li, Wei He, Jingdong Wang, Hua Huang
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang
arXiv:2607. 04447v1 Announce Type: new Abstract: Local causal discovery is a scalable alternative to global structure learning.
By Seong Woo Ahn, Alessandro Leite, Jos\'e Lucas De Melo Costa, Fabrice Popineau, Bich-Li\^en Doan, Arpad Rimmel
arXiv:2607. 12127v1 Announce Type: new Abstract: Learning-based methods for the traveling salesman problem (TSP) are often evaluated through the tours produced after decoding or search, but the learned object itself frequently lives in a surrogate space such as heatmaps, assignments, construction policies, or search-guidance scores.
By Ke Sun, Xinyuan Zhang, Xinwu Qian