Recent research in causal inference has made important progress in addressing challenges to the external validity of trial findings. Such methods weight trial participant data to more closely resemble the distribution of effect-modifying covariates in a well-defined target population. In the presence of participant non-adherence to study medication, these methods effectively transport an intention-to-treat effect that averages over heterogeneous compliance behaviors. In this paper, we develop a principal stratification framework to identify causal effects conditioning on both on compliance behavior and membership in the target population. We also develop non-parametric efficiency theory for and construct efficient estimators of such "transported" principal causal effects and characterize their finite-sample performance in simulation experiments. While this work focuses on treatment non-adherence, the framework is applicable to a broad class of estimands that target effects in clinically-relevant, possibly latent subsets of a target population.

翻译：因果推断领域的最新研究在应对试验结果外部有效性的挑战方面取得了重要进展。这类方法对试验参与者数据进行加权，使其在效应修饰协变量的分布上更接近特定目标人群。当参与者对研究药物的依从性存在缺失时，这些方法能够有效迁移一个对异质性依从行为进行平均的意向治疗效应。本文构建了一个主分层框架，用于识别同时以依从行为和目标人群成员资格为条件的因果效应。我们还推导了此类"迁移后"主因果效应的非参数效率理论，并构建了其高效估计量，通过模拟实验刻画了其有限样本性能。尽管本研究聚焦于治疗不依从问题，该框架适用于一类广泛的估计目标，这些目标旨在评估目标人群中具有临床相关性（可能为潜在子集）的效应。