This paper proposes a novel method for causal inference in panel settings with network-valued outcomes by introducing the score augmented Frobenius distance, a metric that compares networks after sorting their adjacency matrices by node-level structural scores. These scores, which incorporate both observed covariates and structural features (e.g., centrality), serve to align nodes across networks under the assumption that structural roles, rather than identities, drive treatment effects. This sorting induces an equivalence class over node permutations, allowing valid comparisons between networks with unobserved heterogeneity. This paper demonstrates how score-augmented Frobenius distance can be applied to extend standard difference-in-differences and synthetic control methods to settings where observations are networks. The framework applies to a variety of empirical settings, including social networks, trade networks, and institutional relationships, where interventions affect structural properties rather than specific node labels.