Standard approaches in generalizability often focus on generalizing the intent-to-treat (ITT). However, in practice, a more policy-relevant quantity is the generalized impact of an intervention across compliers. While instrumental variable (IV) methods are commonly used to estimate the complier average causal effect (CACE) within samples, standard approaches cannot be applied to a target population with a different distribution from the study sample. This paper makes several key contributions. First, we introduce a new set of identifying assumptions in the form of a population-level exclusion restriction that allows for identification of the target complier average causal effect (T-CACE) in both randomized experiments and observational studies. This allows researchers to identify the T-CACE without relying on standard principal ignorability assumptions. Second, we propose a class of inverse-weighted estimators for the T-CACE and derive their asymptotic properties. We provide extensions for settings in which researchers have access to auxiliary compliance information across the target population. Finally, we introduce a sensitivity analysis for researchers to evaluate the robustness of the estimators in the presence of unmeasured confounding and extend existing tests to evaluate instrument validity in this context. We illustrate our proposed method through extensive simulations and a study evaluating the impact of deep canvassing on reducing exclusionary attitudes.

翻译：在因果效应推广的标准化方法中，通常关注意图处理效应（ITT）的推广。然而在实践中，更具政策相关性的指标是干预措施对依从者的总体影响。虽然工具变量（IV）方法常用于估计样本内依从者平均因果效应（CACE），但标准方法无法应用于与研究样本分布不同的目标人群。本文做出若干关键贡献。首先，我们提出一类新的识别假设——总体层面的排他性约束，这使得在随机实验和观察性研究中均能识别目标依从者平均因果效应（T-CACE）。该方法无需依赖标准的主层可忽略性假设即可识别T-CACE。其次，我们提出一类T-CACE的逆概率加权估计量，并推导其渐近性质。针对研究者可获取目标人群辅助依从信息的情形，我们给出扩展方案。最后，我们引入敏感性分析方法，使研究者能够评估存在未测量混杂时估计量的稳健性，并扩展现有检验方法以评估该情境下的工具变量有效性。通过大量模拟实验和一项评估深度游说对减少排外态度影响的研究，我们验证了所提方法的有效性。