We consider Dynamic Treatment Regimes (DTRs) with One Sided Noncompliance that arise in applications such as digital recommendations and adaptive medical trials. These are settings where decision makers encourage individuals to take treatments over time, but adapt encouragements based on previous encouragements, treatments, states, and outcomes. Importantly, individuals may not comply with encouragements based on unobserved confounders. For settings with binary treatments and encouragements, we provide nonparametric identification, estimation, and inference for Dynamic Local Average Treatment Effects (LATEs), which are expected values of multiple time period treatment contrasts for the respective complier subpopulations. Under standard assumptions in the Instrumental Variable and DTR literature, we show that one can identify Dynamic LATEs that correspond to treating at single time steps. Under an additional cross-period effect-compliance independence assumption, which is satisfied in Staggered Adoption settings and a generalization of them, which we define as Staggered Compliance settings, we identify Dynamic LATEs for treating in multiple time periods.

翻译：我们考虑在数字推荐和自适应医学试验等应用中出现的具有单侧不依从性的动态处理方案（DTRs）。这类场景下，决策者随时间推移鼓励个体接受处理，但会根据先前的鼓励、处理、状态和结果调整鼓励策略。关键挑战在于，个体可能基于未观测混杂因素不依从鼓励。针对二元处理和鼓励设定，我们为动态局部平均处理效应（LATEs）提供了非参数识别、估计与推断方法，这些效应衡量的是各依从子群体在多时间间隔的处理效果对比期望值。在工具变量和DTR文献的标准假设下，我们证明可以识别对应单时间步处理的动态LATEs。在满足跨期效应-依从独立性假设（该假设在交错采纳设定及其推广形式——我们定义为交错依从设定——中成立）时，我们进一步识别出多时期动态处理的LATEs。