Principal stratification is an effective framework addressing intermediate variables in causal inference. However, point identification of the principal causal effects (PCEs) often requires the untestable principal ignorability (PI) assumption. This article develops a nonparametric sensitivity analysis framework for evaluating PI violations. We introduce a margin-free bounding factor parameterized by the selection and outcome relative risks of an unmeasured confounder. Using this bounding factor, we derive sharp nonparametric bounds for each PCE. We prove that these bounds nest within the worst-case nonparametric bounds with and without the monotonicity assumption. We then discuss Cornfield-type conditions and principal E-values that quantify the minimum joint magnitude of unmeasured confounding required to nullify the target PCE. Furthermore, we generalize this methodology to principal generalized causal effects, extending the sensitivity bounds and falsification thresholds to the recent pairwise comparison estimands evaluated over a product space.

翻译：主分层是因果推断中处理中间变量的有效框架。然而，主因果效应（PCEs）的点识别通常需要不可检验的主忽略性（PI）假设。本文开发了一个用于评估PI违反情况的非参数敏感性分析框架。我们引入了一个无边际边界因子，该因子由未测量混杂因素的选择相对风险和结局相对风险参数化。利用这个边界因子，我们为每个PCE推导出尖锐的非参数边界。我们证明这些边界嵌套在单调性假设成立与不成立时的最坏情况非参数边界内。随后，我们讨论了Cornfield型条件和主E值，这些量用于量化使目标PCE失效所需的最小未测量混杂联合强度。此外，我们将此方法推广至主广义因果效应，将敏感性边界和证伪阈值扩展到在乘积空间上评估的最新配对比较估计量。