AI 中文总结
该研究为因果面板估计量开发敏感性分析工作流程，结合里斯表示法与部分\(R^2\)值，分离两条报告路径，推导特定估计量的里斯诊断，经蒙特卡罗测试和实例应用验证，展示了方法在因果面板估计中的有效性。
AI 中文摘要
我们为因果面板估计量开发了一种敏感性分析工作流程，涵盖合成差分、矩阵补全、固定效应插补和组时间平均处理效应。该工作流程将里斯表示遗漏变量偏差界限与部分\(R^2\)稳健性值相结合，并分离出两条报告路径。路径A给出由结果侧和里斯侧部分\(R^2\)值总结的加性或投影混杂的直接敏感性概况。路径B仅在基准计数、α侧对齐、模型检查、依赖性和优势诊断可信时才将观察协变量基准视为辅助数据；否则其主要作用是降级。我们推导了特定估计量的里斯诊断，并阐明哪些是固定权重、目标水平或第一阶段条件的，而不是正则化训练映射的全导数。蒙特卡罗压力测试区分了校准基准设置与优势失败、粗略的α侧基准、基准依赖性、噪声协变量和集中的SDID权重。在加利福尼亚烟草控制面板中，SDID估计值为每人-15.60包；校正后的有限捐赠者安慰剂推断给出标准误差9.49和加一\(p = 0.051\)。重新拟合权重有限差分审计将路径A的无效稳健性值从0.054更改为0.045，低个位数结论不变。县级最低工资应用将相同的概况应用于多队列交错面板。
英文摘要
We develop a sensitivity-analysis workflow for causal panel estimators, covering synthetic difference-in-differences, matrix completion, fixed-effect imputation, and group-time average treatment effects. The workflow combines Riesz-representation omitted-variable-bias bounds with partial-$R^2$ robustness values and separates two reporting routes. Route A gives a direct sensitivity profile for additive or projected confounding summarized by outcome-side and Riesz-side partial $R^2$ values. Route B treats observed-covariate benchmarks as auxiliary data only when benchmark-count, alpha-side alignment, model-check, dependence, and dominance diagnostics are credible; otherwise its main role is demotion. We derive estimator-specific Riesz diagnostics and clarify which are fixed-weight, target-level, or first-stage-conditional rather than full derivatives of regularized training maps. Monte Carlo stress tests distinguish calibrated benchmark settings from dominance failure, coarse alpha-side benchmarks, benchmark dependence, noisy covariates, and concentrated SDID weights. In the California tobacco-control panel, the SDID estimate is $-15.60$ packs per capita; corrected finite-donor placebo inference gives standard error 9.49 and add-one $p=0.051$. A refit-weight finite-difference audit changes the Route A nullification robustness value from 0.054 to 0.045, leaving the low-single-digit conclusion unchanged. A county-level minimum-wage application applies the same profile to a multi-cohort staggered panel.
