Understanding the causal impact of medical interventions is essential in healthcare research, especially through randomized controlled trials (RCTs). Despite their prominence, challenges arise due to discrepancies between treatment allocation and actual intake, influenced by various factors like patient non-adherence or procedural errors. This paper focuses on the Complier Average Causal Effect (CACE), crucial for evaluating treatment efficacy among compliant patients. Existing methodologies often rely on assumptions such as exclusion restriction and monotonicity, which can be problematic in practice. We propose a novel approach, leveraging supervised learning architectures, to estimate CACE without depending on these assumptions. Our method involves a two-step process: first estimating compliance probabilities for patients, then using these probabilities to estimate two nuisance components relevant to CACE calculation. Building upon the principal ignorability assumption, we introduce four root-n consistent, asymptotically normal, CACE estimators, and prove that the underlying mixtures of experts' nuisance components are identifiable. Our causal framework allows our estimation procedures to enjoy reduced mean squared errors when exclusion restriction or monotonicity assumptions hold. Through simulations and application to a breastfeeding promotion RCT, we demonstrate the method's performance and applicability.

翻译：理解医疗干预的因果效应在医疗健康研究中至关重要，尤其是在随机对照试验（RCT）中。尽管RCT具有显著优势，但由于治疗分配与实际摄入之间可能存在差异——受患者不依从或程序错误等因素影响——这一方法面临挑战。本文聚焦于依从者平均因果效应（CACE），该指标对于评估依从患者中的治疗效果至关重要。现有方法通常依赖于排除限制和单调性等假设，而这些假设在实践中可能存在问题。我们提出了一种新颖方法，利用监督学习架构来估计CACE，而无需依赖这些假设。该方法分为两步：首先估计患者的依从概率，随后利用这些概率估计与CACE计算相关的两个干扰成分。基于主忽略性假设，我们引入了四个根n一致、渐近正态的CACE估计量，并证明了其背后混合专家模型的干扰成分具有可识别性。我们的因果框架使得估计过程在排除限制或单调性假设成立时，能够获得更低的均方误差。通过仿真实验和一项母乳喂养促进RCT的应用，我们展示了该方法的性能与适用性。