{
  "abstract": "In high-dimensional semi-supervised linear regression, prediction-powered inference (PPI) corrects an external predictor with a rectifier estimated from the labeled data. In a linear model, however, this rectifier cancels the predictor: PPI and PPI++ reduce to ordinary least squares and can inflate variance when the predictor is close to the oracle. We propose the Debiased External-model-Assisted Lasso (DEAL), which routes the external estimator and the unlabeled covariates into the variance of a debiased estimator, with a bias-aware, cross-fitted shrinkage step that adapts across target-only, near-oracle, and biased-but-informative regimes. We prove coordinate-wise asymptotic normality with an adaptive variance, extend validity to the projection parameter under misspecification and nonlinear labelers, and show that, at a common unlabeled budget, DEAL intervals are shorter than those of debiased Lasso, PPI, and PPI++; a shift-aware variant preserves coverage under covariate shift. In simulations, DEAL intervals are 0.49-0.87 of the debiased-Lasso length, and across six real-data applications spanning astronomy, chemistry, proteomics, and oncology, the last using a large-language-model oracle, they tighten in every case, with median length ratios of 0.23-0.53.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.15602",
  "author_total": 3,
  "authors": [
    {
      "name": "Hongzhe Zhang"
    },
    {
      "name": "Hanxuan Ye"
    },
    {
      "name": "Hongzhe Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2se6q3myswhc7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.15602",
  "fulltext_url": null,
  "id": "2se6q3myswhc7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.15602",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7164899401",
  "pdf_url": "https://arxiv.org/pdf/2606.15602",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2se6q3myswhc7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2606.15602",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.15602"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Bias-Aware External-Model-Assisted Inference in High-Dimensional Regression",
  "title_text": "Bias-Aware External-Model-Assisted Inference in High-Dimensional Regression",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/2se6q3myswhc7",
  "venue": "arXiv",
  "versions": []
}