{
  "abstract": "Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption. In this work, we relax this assumption by allowing each unit's potential outcomes to depend on other units' treatments and covariates. In this setting, propensity-score reweighting does not restore weighted exchangeability, and existing methods may fail to achieve valid coverage. To address this issue, we develop interference-adjusted weighted conformal prediction that accounts for interference by constructing an observable upper bound on the ideal and unobserved conformal $p$-value under the target intervention. The resulting prediction sets provide finite-sample marginal coverage guarantees for counterfactual outcomes and individual treatment effects in both transductive and inductive settings. We also derive a sharper construction when intervention-induced changes in nonconformity scores are bounded. Numerical experiments show that our methods preserve nominal coverage, whereas existing methods may not.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.15254",
  "author_total": 2,
  "authors": [
    {
      "name": "Matteo Zecchin"
    },
    {
      "name": "Osvaldo Simeone"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9kb0zpaa6p0p6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.15254",
  "fulltext_url": null,
  "id": "9kb0zpaa6p0p6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.15254",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7213256080",
  "pdf_url": "https://arxiv.org/pdf/2609.15254",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9kb0zpaa6p0p6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:29Z",
      "native_id": "2609.15254",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.15254"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Conformal Individual Treatment Effect Estimation under Networked Interference",
  "title_text": "Conformal Individual Treatment Effect Estimation under Networked Interference",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:36Z",
  "url": "https://research.draconitic.com/paper/9kb0zpaa6p0p6",
  "venue": "arXiv",
  "versions": []
}