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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.19155 |
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| _version_ | 1866908593186406400 |
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| author | Ramírez-Ayerbe, Jasone Frejinger, Emma |
| author_facet | Ramírez-Ayerbe, Jasone Frejinger, Emma |
| contents | In this paper, we consider contextual stochastic optimization problems under endogenous uncertainty, where decisions affect the underlying distributions. To implement such decisions in practice, it is crucial to ensure that their outcomes are interpretable and trustworthy. To this end, we compute relative counterfactual explanations that provide practitioners with concrete changes in the contextual covariates required for a solution to satisfy specific constraints. Whereas relative explanations have been introduced in prior literature, to the best of our knowledge this is the first work focusing on problems with binary decision variables and endogenous uncertainty. We propose a methodology that uses the Wasserstein distance as a regularization term, which leads to a reduction in computation times compared to its unregularized counterpart. We illustrate the method using a choice-based competitive facility location problem and present numerical experiments that demonstrate its ability to efficiently compute sparse and interpretable explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location Ramírez-Ayerbe, Jasone Frejinger, Emma Optimization and Control In this paper, we consider contextual stochastic optimization problems under endogenous uncertainty, where decisions affect the underlying distributions. To implement such decisions in practice, it is crucial to ensure that their outcomes are interpretable and trustworthy. To this end, we compute relative counterfactual explanations that provide practitioners with concrete changes in the contextual covariates required for a solution to satisfy specific constraints. Whereas relative explanations have been introduced in prior literature, to the best of our knowledge this is the first work focusing on problems with binary decision variables and endogenous uncertainty. We propose a methodology that uses the Wasserstein distance as a regularization term, which leads to a reduction in computation times compared to its unregularized counterpart. We illustrate the method using a choice-based competitive facility location problem and present numerical experiments that demonstrate its ability to efficiently compute sparse and interpretable explanations. |
| title | Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2506.19155 |