发表机构
Toyo University（东洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出用候选因果图集明确控制变量假设，分解稳健性分析中的离散度，并揭示汇集评估的误导性，提供R包实现。
AI 中文摘要
模型稳健性分析在规范的多重宇宙中估计效应，该多重宇宙将识别声明估计量的控制集与以中介变量或碰撞变量为条件的控制集汇集在一起。我们提出将有争议的控制变量的竞争性假设表述为一小组候选因果图，枚举每个图所许可的调整集，并报告以每个图为条件的稳健性指标。一个有限混合恒等式将许可多重宇宙的离散度分解为图内和图间成分；图间份额是一个条件性描述性总结，其解读取决于候选集、权重和共同的估计量。模拟研究考察了误导性的汇集稳健性评估以及该分解的局限性。应用于飓风死亡人数、职业培训和工会工资的案例表明，脆弱性在每个图中都存在，未经许可的规范会产生不稳定性，以及一个脆弱性判定掩盖了每个调整识别的候选世界中的显著溢价。一个R软件包实现了该工作流程。
英文摘要
Model robustness analysis estimates an effect across a multiverse of specifications that pools control sets identifying the declared estimand with sets that condition on mediators or colliders. We propose stating rival assumptions about contested controls as a small set of candidate causal graphs, enumerating the adjustment sets each graph licenses, and reporting robustness metrics conditional on each graph. A finite-mixture identity splits the licensed multiverse's dispersion into within-graph and between-graph components; the between-graph share is a conditional descriptive summary whose reading depends on the candidate set, the weights, and a common estimand. Simulations examine misleading pooled robustness assessments and the limits of the decomposition. Applications to hurricane fatalities, job training, and union wages show fragility that survives every graph, instability produced by unlicensed specifications, and a fragility verdict concealing a significant premium in each adjustment-identified candidate world. An R package implements the workflow.
Comments72 pages, 10 figures, 9 tables (supplemental tables included). R package dagmv: https://github.com/sokubo/dagmv; replication archive: https://github.com/sokubo/paper-multiverse-dag-replication
