发表机构
Tricon Infotech（特里康信息科技公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出MCES框架，通过汇集8种数学传统的11种因果方法的收敛证据得分，对观测数据中候选驱动因素与结果的相关性排名，为无单一最优方法的场景提供方法无关的默认方案。
AI 中文摘要
从观测数据中推断因果关系的从业者通常依赖单一方法，并将其输出视为因果真理。近期的工具会为数据集选择最优方法，近期的集成方法则将多个因果发现算法聚合为一张图，但很少有研究汇集不同数学传统（包括非因果传统）的证据。我们提出多方法因果证据合成（Multi-Method Causal Evidence Synthesis, MCES）框架，该框架对观测系统中最可能与一组结果相关的候选驱动因素及其证据强度进行排名。MCES在观测面板数据上运行来自8种数学传统的11种方法，并将其输出汇集为收敛证据得分（Convergent Evidence Score, CES），这是一种线性意见池。CES量化了不同分析视角下证据的收敛程度：具有不同假设的方法指向同一驱动因素-结果关系的程度。它不主张干预主义意义上的因果识别，而是支持假设优先级排序，而非可转移的因果概率。MCES首先应用结构行为分解（Structural-Behavioral Decomposition）去除定义性（代数）关系，随后运行所有方法，将输出归一化至[0,1]区间并进行汇集。我们将MCES与方法选择、结构集成、预测集成及文献合成区分开。通过带有嵌入真实值的合成数据、Sachs蛋白质信号基准、6个贝叶斯网络结构基准及另外两个合成域，我们表明MCES能将真实边排在靠前位置（主场景中Precision@5=1.0，Precision@10=0.96），且达到中等或更高收敛度的空对经验率较低。我们的核心观点并非汇集方法优于每一种单一方法，而是在评估场景中没有单一方法始终最优，因此MCES提供了一种与方法无关的默认方案。
英文摘要
Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tools select an optimal method for a dataset, and recent ensembles aggregate multiple causal-discovery algorithms into one graph, but little work pools evidence across different mathematical traditions, including non-causal ones. We present Multi-Method Causal Evidence Synthesis (MCES), a framework that ranks which candidate drivers in an observational system are most likely relevant to a set of outcomes, and with what strength of evidence. MCES runs eleven methods across eight mathematical traditions on observational panel data and pools their outputs into a Convergent Evidence Score (CES), a linear opinion pool. CES quantifies convergence of evidence across analytical lenses: the degree to which methods with different assumptions point to the same driver-outcome relationship. It does not claim causal identification in the interventionist sense; it supports hypothesis prioritization, not a transferable probability of causation. MCES first applies Structural-Behavioral Decomposition to remove definitional (algebraic) relationships, then runs all methods, normalizes outputs to [0,1], and pools them. We distinguish MCES from method selection, structural ensembles, prediction ensembles, and literature synthesis. Using synthetic data with embedded ground truth, the Sachs protein-signaling benchmark, six Bayesian-network structure benchmarks, and two further synthetic domains, we show MCES ranks true edges near the top (Precision@5 = 1.0, Precision@10 = 0.96 on the primary scenario), with a low empirical rate of null pairs reaching Moderate-or-higher convergence. Our central point is not that the pool beats every individual method, but that no single method is uniformly best across the evaluated scenarios, so MCES offers a method-agnostic default.
Comments36 pages, 4 figures, 17 tables. Reference implementation available from the authors on request
