Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data
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多方法因果证据综合：以跨方法趋同证据排序候选驱动因素
- 研究问题
- 不同数学传统的方法对观察数据给出不同关系证据时，如何综合这些证据排序候选驱动因素，为后续假设研究确定优先级？
- 方法与思路
- 作者先移除定义性代数关系，再运行十一种方法，将输出归一化后通过线性意见池构造跨方法趋同证据分数。
- 材料陈述的贡献
- 摘要称，该框架在所述基准中将真实关系排在前列；分数用于衡量证据趋同，而非干预意义上的识别或因果概率。
- 阅读关注点
- 核对代数关系剔除、归一化和汇总权重的规则，以及方法间依赖、阈值选择和基准真值如何影响排序表现。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；作者明确不主张因果识别，摘要中的排序表现不能转译为因果概率。
原文摘要
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.