发表机构
Arizona State University（亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DECO框架，利用观测数据中的方向性证据在精确优化前缩减DAG学习的搜索空间，理论证明可指数级减少父集配置，实验验证了在保持结构恢复性能的同时显著降低计算负担。
AI 中文摘要
从观测数据中学习有向无环图（DAG）是一个具有挑战性的组合问题，因为候选父集配置的数量呈指数增长。现有的基于精确评分的方法通常需要计算密集的组合搜索，而基于约束的方法随着图规模和条件集复杂性的增加可能变得不可靠或计算要求过高。我们开发了一个非参数混合框架，称为DECO（方向性证据引导的配置优化），它从观测数据中提取依赖性和方向性证据，在精确优化之前构建可接受的父集。它通过消除经验上不支持的父配置来缩减优化搜索空间，同时保留所有合理边方向灵活性。理论分析建立了可接受父集配置空间的指数级缩减，并量化了有界边级遗漏如何影响保留真实父结构的概率。在基准贝叶斯网络和合成离散及连续DAG上的实验表明，实现了显著的搜索空间缩减，同时获得了有竞争力的结构恢复性能，在许多评估设置中具有有利的结构汉明距离。这些结果表明，方向性证据可以提供一种有效的预处理机制，在不要求固定参数化结构模型的情况下减少精确DAG学习的计算负担。
英文摘要
Learning a directed acyclic graph (DAG) from observational data is a challenging combinatorial problem due to the exponential growth in the number of candidate parent-set configurations. Existing exact score-based methods often require computationally intensive combinatorial search, whereas constraint-based methods can become unreliable or computationally demanding as graph size and conditioning-set complexity increase. We develop a non-parametric hybrid framework, referred to as DECO (Directional Evidence-guided Configuration Optimization), that extracts dependency and directional evidence from observation data to construct admissible parent sets prior to exact optimization. It reduces the optimization search space by eliminating empirically unsupported parent configurations while preserving flexibility for all plausible edge orientations. Theoretical analysis establishes an exponential reduction in the admissible parent-set configuration space and quantifies how bounded edge-level omission affects the probability of retaining the true parent structure. Experiments on benchmark Bayesian networks and synthetic discrete and continuous DAGs demonstrate substantial search-space reduction while achieving competitive structure-recovery performance, with favorable structural Hamming distance across many evaluated settings. These results show that directional evidence can provide an effective preprocessing mechanism for reducing the computational burden of exact DAG learning without requiring a fixed parametric structural~model.
