Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal causal effects (PCE) in such settings. However, existing PS methods primarily focus on settings with binary intermediate variables. We propose a novel approach to estimate PCE with continuous intermediate variables in the context of stepped wedge cluster randomized trials (SW-CRTs). Our method leverages the time-varying treatment assignment in SW-CRTs to calibrate sensitivity parameters and identify the PCE under realistic assumptions. We demonstrate the application of our approach using data from a cohort SW-CRT evaluating the effect of a crowdsourcing intervention on HIV testing uptake among men who have sex with men in China, with social norms as a continuous intermediate variable. The proposed methodology expands the scope of PS to accommodate continuous variables and provides a practical tool for causal inference in SW-CRTs.

翻译：因果推断在存在中间变量的情况下是许多应用中的难题。主分层（PS）为此类情境下估计主因果效应（PCE）提供了框架。然而，现有的PS方法主要关注二元中间变量的情形。我们提出了一种在阶梯楔形整群随机试验（SW-CRTs）背景下估计连续中间变量PCE的新方法。该方法利用SW-CRTs中随时间变化的治疗分配来校准敏感性参数，并在现实假设下识别PCE。我们通过一项评估众包干预对中国男男性行为者HIV检测接受度影响的队列SW-CRT数据，以社会规范作为连续中间变量，展示了该方法的应用。所提出的方法论拓展了PS的适用范围以纳入连续变量，并为SW-CRTs中的因果推断提供了实用工具。