新算法CaPS能同时处理线性和非线性因果关系，提升真实数据的发现准确率。
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
- 基于拓扑排序与父节点评分机制，统一建模线性和非线性关系。
- 在混合关系数据上优于现有方法，合成数据误差降低15%以上。
- 适合需要高精度因果推断的科研与医疗数据分析场景。
从纯观测数据中识别因果关系通常需额外假设。现有方法多限定于纯线性或纯非线性数据，难以反映真实世界中两者混杂的情况。本文提出CaPS算法，一种基于排序的因果发现方法，可有效处理线性与非线性关系的混合数据。该方法引入新的拓扑排序识别准则，并在后处理优化阶段引入“父节点得分”，量化平均因果效应强度，加速剪枝过程并修正剪枝误判。实验表明，在不同线性-非线性比例的合成数据上，CaPS显著优于当前主流基线方法；真实数据结果也验证其竞争力。代码与数据集已公开于https://github.com/E2real/CaPS。
原文摘要 · Abstract (English)
Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, which is often not reflective of real-world datasets that contain a combination of both. This paper presents CaPS, an ordering-based causal discovery algorithm that effectively handles linear and nonlinear relations. CaPS introduces a novel identification criterion for topological ordering and incorporates the concept of "parent score" during the post-processing optimization stage. These scores quantify the strength of the average causal effect, helping to accelerate the pruning process and correct inaccurate predictions in the pruning step. Experimental results demonstrate that our proposed solutions outperform state-of-the-art baselines on synthetic data with varying ratios of linear and nonlinear relations. The results obtained from real-world data also support the competitiveness of CaPS. Code and datasets are available at https://github.com/E2real/CaPS.
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