{
  "abstract": "Classical variance-based Global Sensitivity Analysis (GSA) assumes that the input--output mechanism can be repeatedly evaluated under a designed sampling scheme, which is infeasible when only a given sample of observations is available. We propose MM--GSA, a metamodel-based approach to GSA from observational data, in which supervised learning approximates the systematic input--output relationship. MM--GSA combines two complementary perspectives on input relevance: a model-agnostic estimator of the first-order Sobol' index, quantifying the contribution of an input to the variability of the systematic response, and a new trigger-based structural index, quantifying how predictive performance depends on the availability of a predictor across alternative predictor subsets. We establish consistency for both estimators and a variable-selection property for the structural index under input independence. Monte Carlo experiments and an NHANES application illustrate their finite-sample behavior and show that the two measures provide complementary information on input relevance.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40342",
  "author_total": 1,
  "authors": [
    {
      "name": "Giulia Vannucci"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/32teyb80g2gqt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40342",
  "fulltext_url": null,
  "id": "32teyb80g2gqt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40342",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7215052079",
  "pdf_url": "https://arxiv.org/pdf/2609.40342",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/32teyb80g2gqt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:58Z",
      "native_id": "2609.40342",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40342"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach",
  "title_text": "Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:58Z",
  "url": "https://research.draconitic.com/paper/32teyb80g2gqt",
  "venue": "arXiv",
  "versions": []
}