In randomized controlled trials, the complier average causal effect (CACE) parameter is often of policy interest because it pertains to intervention effects for study units that comply with their research assignments and receive a meaningful dose of treatment services. Causal inference methods for identifying and estimating the CACE parameter using an instrumental variables (IV) framework are well established for designs with a single treatment and control group. This article uses a parallel IV framework to discuss and build on the much smaller literature on estimation of CACE parameters for designs with multiple treatment groups. The key finding is that the conditions to identify and estimate CACE parameters are much more complex for multiarmed designs and may not be tractable in some cases. Practical steps are provided on how to proceed, and a case study demonstrates key issues. The results suggest that ensuring compliance is particularly important in multiarmed trials so that intention-to-treat estimates on the offer of intervention services (which can be identified) can provide meaningful information on the CACE parameters.

在随机对照试验中，编者平均因果效应 (CACE) 参数通常具有政策意义，因为它涉及遵守其研究任务并接受有意义剂量治疗服务的研究单位的干预效果。使用工具变量 (IV) 框架识别和估计 CACE 参数的因果推断方法已经很好地建立在具有单一治疗组和对照组的设计中。本文使用并行 IV 框架来讨论和构建关于为多个治疗组设计的 CACE 参数估计的小得多的文献。关键发现是，对于多臂设计，识别和估计 CACE 参数的条件要复杂得多，并且在某些情况下可能难以处理。提供了有关如何进行的实际步骤，案例研究展示了关键问题。结果表明，确保依从性在多臂试验中尤为重要，以便对干预服务（可以识别）提供的意向治疗估计可以提供有关 CACE 参数的有意义的信息。