基于评分匹配的因果发现新方法，可准确推断离散数据的因果顺序。
Ordering-based Causal Discovery via Generalized Score Matching
- 利用离散数据的评分函数设计新型叶节点判别准则
- 在模拟与真实数据上均能准确恢复因果顺序
- 显著提升现有因果发现方法的性能，适合离散数据场景
从纯观测数据中学习有向无环图（DAG）结构在多个科学领域仍是一个长期挑战。近年来的研究利用数据分布的评分，通过叶节点检测初步确定底层DAG的拓扑顺序，随后进行边剪枝以完成图恢复。本文将原本仅适用于连续数据的评分匹配框架扩展至离散数据，并提出一种基于离散评分函数的新叶节点判别准则。通过模拟与真实世界实验验证，该理论能够从观测的离散数据中准确推断出真实的因果顺序，所识别的顺序可显著提升现有因果发现基线方法在几乎所有设置下的准确率。
原文摘要 · Abstract (English)
Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains. An emerging line of research leverages the score of the data distribution to initially identify a topological order of the underlying DAG via leaf node detection and subsequently performs edge pruning for graph recovery. This paper extends the score matching framework for causal discovery, which is originally designated for continuous data, and introduces a novel leaf discriminant criterion based on the discrete score function. Through simulated and real-world experiments, we demonstrate that our theory enables accurate inference of true causal orders from observed discrete data and the identified ordering can significantly boost the accuracy of existing causal discovery baselines on nearly all of the settings.
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