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We propose a methodology that uses the Wasserstein distance as a regularization term in the objective. Beyond improving tractability, this regularization yields explanations with desirable structural properties: it produces sparser counterfactuals, induces smoother transitions in the underlying choice distributions, and keeps the counterfactual behavior close to realistic demand patterns. We illustrate the method using a choice-based competitive facility location problem and present numerical experiments that demonstrate its ability to efficiently compute sparse, plausible, and interpretable explanations. We further validate the framework on a real-world case study of electric vehicle charging station planning in Montreal, where the explanations reveal the minimal capacity investments and environmental conditions required to justify including a candidate location in the charging network.
From: Jasone RamÃrez-Ayerbe [view email]
[v1]
Mon, 23 Jun 2025 21:46:41 UTC (194 KB)
[v2]
Fri, 18 Jul 2025 19:33:48 UTC (195 KB)
[v3]
Tue, 14 Oct 2025 21:35:03 UTC (111 KB)
[v4]
Wed, 17 Jun 2026 18:17:23 UTC (10,503 KB)
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