WHAT DO TIME-SERIES EXPLANATIONS ACTUALLY EXPLAIN?

An attribution over a time-series window may indicate that a cell matters, but not in what sense. Under autocorrelation, a cell can influence the prediction almost entirely through the later cells into which its effect propagates, yet a method that ranks by association credits it in exactly the same way as it credits a cell the model reads directly. Existing faithfulness scores cannot separate these cases, because they test an explanation against a single, undifferentiated notion of importance. We characterize these effects on the Model Dependency DAG (MDDAG): we define a direct effect as a directed input–output edge on the MDDAG, and a total effect as the corresponding input–output dependency obtained when later cells are allowed to respond. Against either reference, we define OMIC, a bounded concordance score between an explainer's claimed and denied edges (effects), together with a ranking curve and its normalized area. The mediation residual serves as a diagnostic: an explainer that emphasizes mediated relevance will score highly on total effect but poorly on direct effect. We validate the references and the score on a synthetic VAR with known coefficients and apply them to known time-series explainers.