Abstract：
Comparing different medications is complicated when adherence to these medications differs. We can overcome the adherence issue by assessing effectiveness under sustained use, as in usual causal \'per-protocol\' estimands. However, when sustained use is challenging to satisfy in practice, the usefulness of these estimands can be limited. Here we propose a different class of estimands: separable effects for adherence. These estimands compare modified medications, holding fixed a component responsible for non-adherence. Under assumptions about treatment components\' mechanisms of effect, a separable effects estimand can quantify the effectiveness of medication initiation strategies on an outcome of interest under the adherence mechanism of one of the medications. These assumptions are amenable to interrogation by subject-matter experts and can be evaluated using causal graphs. We describe an algorithm for constructing causal graphs for separable effects, illustrate how these graphs can be used to reason about assumptions required for identification, and provide semi-parametric weighted estimators.
摘要：
当坚持这些药物时，比较不同的药物是复杂的。我们可以通过评估持续使用的有效性来克服依从性问题，就像通常的因果关系一样。然而,当持续使用在实践中难以满足时，这些资产的有用性可能是有限的。在这里，我们提出了一种不同的定义：依从性的可分离效应。这些资产比较了改良药物，持有固定的组件负责不遵守。在关于治疗成分作用机制的假设下，一种可分离的效果评估，可以量化药物启动策略对其中一种药物的依从性机制下感兴趣的结果的有效性。这些假设适合主题专家的询问，并且可以使用因果图进行评估。我们描述了一种构建可分离效应因果图的算法，说明如何使用这些图形来推理识别所需的假设，并提供半参数加权估计。
