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Abstract:A well-defined distance on the parameter space is key to evaluating estimators, ensuring consistency, and building confidence sets. While there are typically standard distances to adopt in a continuous space, this is not the case for combinatorial parameters such as graphs that represent statistical models. Defined on the graphs alone, existing proposals like the structural Hamming distance ignore the structure of the model space and can thus exhibit undesirable behaviors. We propose a model-oriented framework for defining the distance between graphs that is applicable across different graph classes. Our approach treats each graph as a statistical model and organizes the graphs in a partially ordered set based on model inclusion. This induces a neighborhood structure, from which we define the model-oriented distance as the length of a shortest path through neighbors, yielding a metric in the space of graphs. We apply this framework to probabilistic undirected graphs, causal directed acyclic graphs, {causal acyclic directed mixed graphs}, probabilistic completed partially directed acyclic graphs, and causal maximally oriented partially directed acyclic graphs. We analyze theoretical and empirical behaviors of the model-oriented distance. By exploiting the underlying poset structures, we develop algorithms for computing and bounding the proposed distance that scale to moderate-sized graphs. Finally, we showcase its utility for quantifying the robustness of adjustment sets to errors in specifying the causal graph.

Submission history

From: Armeen Taeb [view email]
[v1] Thu, 13 Nov 2025 18:50:21 UTC (922 KB)
[v2] Fri, 30 Jan 2026 00:08:33 UTC (2,267 KB)
[v3] Tue, 30 Jun 2026 16:15:17 UTC (2,832 KB)