提出新方法挖掘变量间高阶因果关系，提升小样本下因果图准确性。
Causal Discovery on Higher-Order Interactions
- 基于高阶边结构设计新聚合算法，突破传统仅看单边置信度的局限
- 在低样本量和高维场景下优于现有方法，显著提升因果图可靠性
- 适合小样本、高维数据中的因果发现任务，尤其对生物医学等稀缺数据领域有用
因果发现通过结合数据与专家知识，学习变量间因果关系的有向无环图（DAG）。当数据稀缺时，通常采用自助采样（bagging）来评估平均DAG的置信度，但现有研究对聚合步骤关注不足——当前平均DAG仅依赖各边置信度，忽略了复杂的高阶边结构。本文提出一种基于高阶结构的新型理论框架，并设计新的DAG聚合算法。通过模拟实验分析了该方法的优势与局限性。结果表明，该方法在计算效率与实际效果上均表现优异，在低样本量和高维条件下显著超越现有先进方法。
原文摘要 · Abstract (English)
Causal discovery combines data with knowledge provided by experts to learn the DAG representing the causal relationships between a given set of variables. When data are scarce, bagging is used to measure our confidence in an average DAG obtained by aggregating bootstrapped DAGs. However, the aggregation step has received little attention from the specialized literature: the average DAG is constructed using only the confidence in the individual edges of the bootstrapped DAGs, thus disregarding complex higher-order edge structures. In this paper, we introduce a novel theoretical framework based on higher-order structures and describe a new DAG aggregation algorithm. We perform a simulation study, discussing the advantages and limitations of the proposed approach. Our proposal is both computationally efficient and effective, outperforming state-of-the-art solutions, especially in low sample size regimes and under high dimensionality settings.
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