An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function-based and weighting methods. We illustrate the proposed sensitivity analyses using several outcome types from the JOBS II study, and provide code in the R-package PIsens.

翻译：识别主因果效应（常用于存在不依从性的场景）的一个重要策略是采用主可忽略性（PI）假设。由于PI不可检验，评估效应估计对其违背的敏感程度至关重要。本文聚焦于常见的一侧不依从性场景（即存在依从者与非依从者两个主分层）开展此项任务。在PI假设下，依从者与非依从者在控制条件下具有相同的给定协变量的结果均值函数。为进行敏感性分析，我们允许该函数在依从者与非依从者之间以多种方式存在差异，这些差异通过比值比、广义比值比、均值比或标准化均值差敏感性参数进行索引。针对基于PI的多种主流分析方法（包括结果回归、基于影响函数的方法以及加权方法），我们定制了敏感性分析技术（可选用任意敏感性参数）。通过JOBS II研究中的多种结果类型，我们展示了所提出的敏感性分析方法，并提供了R包PIsens中的代码。