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 (IF) based and weighting methods. We illustrate the proposed sensitivity analyses using several outcome types from the JOBS II study. This application estimates nuisance functions parametrically -- for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal -- with a view to inform nonparametric inference.

翻译：识别主成分因果效应（常用于存在非依从性的情境）的重要策略之一是采用主成分可忽略性（PI）假设。由于PI假设不可检验，评估效应估计对其违反的敏感程度至关重要。本文聚焦于常见的单向非依从性情境，其中存在两个主成分层：依从者和非依从者。在PI假设下，依从者和非依从者在控制条件下共享相同的协变量条件结果均值函数。为进行敏感性分析，我们允许该函数在依从者和非依从者之间存在多种形式的差异，这些差异以比值比、广义比值比、均值比或标准化均值差敏感参数为索引。我们针对基于PI的多种主要分析方法（包括结果回归法、基于影响函数（IF）的方法和加权法）定制了敏感性分析技术（可选用任意敏感参数）。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。为简便性和可操作性起见，该应用采用参数化方法估计干扰函数。此外，我们建立了基于IF的估计器在非参数干扰估计下达到渐近正态性的速率条件——旨在为非参数推断提供参考。