The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects (CACE and NACE), we address outcome missingness of the \textit{latent missing at random} type (LMAR, also known as \textit{latent ignorability}). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This paper focuses on effect identification in the presence of LMAR outcome, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a \textit{specific missingness mechanism} assumption and a \textit{principal identification} assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial.

翻译：治疗效应的研究常因不依从性和数据缺失而复杂化。在单侧不依从设定中（关注依从者与非依从者平均因果效应，即CACE和NACE），我们针对"潜在随机缺失"（LMAR，亦称"潜在可忽略性"）类型的结局缺失问题展开研究。即，在给定协变量与分配治疗条件下，缺失机制可能依赖于依从类型。在工具变量（IV）框架处理不依从性时，已有方法通过引入缺失端的排除限制型假设来处理LMAR结局，但尚未有研究提出非IV框架下的解决方案。本文聚焦于LMAR结局下的效应识别问题，旨在灵活适配不同的主识别方法。研究表明，在治疗分配可忽略性与仅存在LMAR条件下，效应不可识别性可归约为一组包含未识别层特异应答概率与结局均值的两联混合方程。这揭示了（除特殊情况外）效应识别通常需要两个附加假设："特定缺失机制"假设与"主识别"假设。该发现为基于这些假设的独立选择来识别效应提供了模板。我们考察了多种特定的缺失机制假设，涵盖文献中已出现的假设及若干新假设。本文还发现了现有假设中的缺陷，并提出修正方案以避免该问题。基于不同假设的结果通过巴尔的摩经验企业试验数据进行了实证分析。