A randomized controlled trial (RCT) is widely regarded as the gold standard for assessing the causal effect of a treatment or intervention, assuming perfect implementation. In practice, however, randomization can be compromised for various reasons, such as one-sided noncompliance. In this paper, we first systematically study the likelihood-based identifiability in an RCT with one-sided noncompliance. This foundational analysis naturally gives rise to the complier general causal effect (CGCE) as the primary estimand. We further develop two estimators for the CGCE: a simple estimator that requires no nonparametric procedures, and an efficient estimator that achieves the semiparametric efficiency bound. Our theoretical analysis shows that, achieving semiparametric efficiency requires only the nuisance estimators to converge in $L_2$-norm, with no restriction on their convergence rates. This rate-free property opens the door to employing many more modern machine learning methods while still guaranteeing efficiency. Comprehensive simulation studies and a real data application are conducted to illustrate the proposed methods and to compare them with existing approaches.

翻译：随机对照试验被广泛视为评估治疗或干预因果效应的金标准，前提是完全实施。然而在实际中，由于单侧不依从等原因，随机化可能受到干扰。本文首先系统研究了存在单侧不依从的随机对照试验中基于似然的可识别性。这一基础分析自然地推导出依从者总因果效应作为主要估计量。我们进一步为CGCE开发了两种估计方法：一种无需非参数程序的简单估计量，以及一种达到半参数效率界的高效估计量。理论分析表明，实现半参数效率仅需干扰估计量在$L_2$范数下收敛，且无收敛速率限制。这一无速率特性为采用更多现代机器学习方法提供了可能，同时仍能保证效率。通过全面仿真研究和实际数据应用，本文对所提方法进行了验证，并与现有方法进行了比较。