{
  "abstract": "Cross-fitting is routine in much of applied research. While conventional confidence intervals that ignore cross-fold dependence are asymptotically valid in several settings, they undercover in many applications that share a common form of nonregularity: from the classic cross-validation problem of testing whether a fitted model outperforms another, to testing for heterogeneous treatment effects with machine learning, to estimating the value of a potentially non-unique optimal treatment regime. Exploiting a new locality condition, I show that a large class of cross-fitting estimators still satisfies a central limit theorem despite the nonregularity, but with an asymptotic variance that must be adjusted for the cross-fold correlation. Then, I propose a method for estimating this correlation and construct new confidence intervals that attain asymptotically nominal coverage. Finally, I show that the proposed confidence intervals attain approximately nominal coverage in a simulation study with random forests and neural networks.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02944",
  "author_total": 1,
  "authors": [
    {
      "name": "Bruno Fava"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c6je3h3m0g1e5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02944",
  "fulltext_url": null,
  "id": "c6je3h3m0g1e5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02944",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "math.ST"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "arxiv",
      "stat.TH"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7220414181",
  "pdf_url": "https://arxiv.org/pdf/2610.02944",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c6je3h3m0g1e5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:50Z",
      "native_id": "2610.02944",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02944"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.ST",
      "subfield": "statistics"
    }
  ],
  "title": "Cross-Fitting Under Nonregularity: Normality and Inference via Locality",
  "title_text": "Cross-Fitting Under Nonregularity: Normality and Inference via Locality",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:56Z",
  "url": "https://research.draconitic.com/paper/c6je3h3m0g1e5",
  "venue": "arXiv",
  "versions": []
}