Staggered treatment adoption arises in the evaluation of policy impact and implementation in a variety of settings. This occurs in both randomized stepped-wedge trials and non-randomized quasi-experimental designs using causal inference methods based on difference-in-differences analysis. In both settings, it is crucial to carefully consider the target estimand and possible treatment effect heterogeneities in order to estimate the effect without bias and in an interpretable fashion. This paper proposes a novel non-parametric approach to this estimation for either setting. By constructing an estimator using two-by-two difference-in-difference comparisons as building blocks with arbitrary weights, the investigator can select weights to target the desired estimand in an unbiased manner under assumed treatment effect homogeneity, and minimize the variance under an assumed working covariance structure. This provides desirable bias properties with a relatively small sacrifice in variance and power by using the comparisons efficiently. The method is demonstrated on toy examples to show the process, as well as in the re-analysis of a stepped wedge trial on the impact of novel tuberculosis diagnostic tools. A full algorithm with R code is provided to implement this method. The proposed method allows for high flexibility and clear targeting of desired effects, providing one solution to the bias-variance-generalizability tradeoff.

翻译：交错引入干预在政策效果评估及其多场景实施中普遍存在，既出现在随机阶梯楔形试验中，也出现在基于双重差分分析因果推断方法的非随机准实验设计中。在这两种场景下，为获得无偏且可解释的效果估计，必须审慎考虑目标参数与可能的处理效应异质性。本文针对两类场景提出一种新颖的非参数估计方法。通过构建以成对双重差分比较为基本模块且可赋予任意权重的估计量，研究者能在假设处理效应同质性下通过选择权重无偏地瞄准目标参数，并在假设的工作协方差结构下最小化方差。该方法通过高效利用比较信息，在方差和检验功效损失相对较小的前提下获得理想的偏倚特性。通过简单示例展示方法论流程，并重新分析一项关于新型结核病诊断工具效果的阶梯楔形试验进行实证验证。文中提供了完整的算法实现R代码。该方法具有高度灵活性，能明确瞄准目标效应，为偏倚-方差-可推广性权衡提供了可行解决方案。