We can formalize these components as subgraphs x are likely causes for the elements in V k y .
we define an explanation of y as a collection of such components: Our approach formalizes this framework through a pipeline (sketched in Figure 1) consisting of three main components, described in detail in the following section: a perturbation model for exercising F locally, a causal inference model for inferring associations between inputs and predictions, and a selection step for partitioning and selecting the most relevant sets of associations.