  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 the usual causal `per-protocol'
estimand. However, when sustained use is challenging to satisfy in practice,
the usefulness of this estimand 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, the
separable effects estimand can eliminate differences in adherence. 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.
