Title: Generalizing causal effects with noncompliance: Application to deep canvassing experiments Show Chinese title

Title: 非依从情况下的因果效应推广：深度动员实验的应用

Abstract: 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. We illustrate our proposed method through extensive simulations and a study evaluating the impact of deep canvassing on reducing exclusionary attitudes. Show Chinese abstract

Abstract: 一般性化方法通常关注于推广意图治疗（ITT）效应。然而，在实践中，一个更具政策相关性的量是干预措施在整个服从者群体中的推广影响。尽管工具变量（IV）方法常用于估计样本内的服从者平均因果效应（CACE），但标准方法无法直接应用于目标人群，因为目标人群的分布可能与研究样本不同。本文做出了几个关键贡献。 首先，我们引入了一组新的识别假设，即以人口层面的排除限制形式存在，这使得在随机试验和观察研究中都能识别目标服从者平均因果效应（T-CACE）。这允许研究人员在不需要依赖标准主要可忽略性假设的情况下识别T-CACE。 其次，我们提出了一类逆加权估计量来估计T-CACE，并推导了它们的大样本性质。我们还提供了扩展，适用于研究人员能够获取目标人群辅助依从信息的情境。 最后，我们引入了一种敏感性分析，使研究人员能够在存在未测量混杂因素的情况下评估估计量的稳健性。 我们通过广泛的模拟研究和一项评估深度拉票对减少排斥态度影响的研究展示了我们提出的方法。