{
  "abstract": "Spurious correlations between nuisance features and outcomes present a fundamental challenge for statistical learning and inference. Counterfactual Data Augmentation (CDA), the practice of resampling nuisance features while preserving features of interest, has emerged as a popular strategy to mitigate such correlations, yet its statistical properties, and its reliance on an untestable validity assumption, remain largely unexplored. We provide a statistical treatment of CDA, termed Statistical CDA (SCDA), with two components. First, casting SCDA as a simulated-moment M-estimator, we establish consistency, asymptotic normality with a sandwich covariance and a finite-augmentation-draw variance correction, oracle efficiency for the target parameter, and the equivalence of iterative and one-shot procedures via stochastic approximation, yielding valid large-sample inference where previously none was available. Second, because the augmentation's validity cannot be tested from the observed data when features are entangled (a fact we prove), we develop a sensitivity analysis that bounds the residual nuisance--outcome dependence by a parameter $Γ$, giving sharp identified intervals for the target with valid confidence intervals and a breakdown frontier; the point-identified theory is the $Γ=0$ special case. Simulations confirm the efficiency gains under validity and the robustness bounds under possible invalidity, placing a widely used augmentation method on a transparent inferential footing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22649",
  "author_total": 2,
  "authors": [
    {
      "name": "Yi-Hui Zhou"
    },
    {
      "name": "George Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ekjzpn73pjaaq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22649",
  "fulltext_url": null,
  "id": "ekjzpn73pjaaq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22649",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7222977148",
  "pdf_url": "https://arxiv.org/pdf/2610.22649",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ekjzpn73pjaaq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:43Z",
      "native_id": "2610.22649",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22649"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Robust Estimation under Spurious Correlation via Statistical Counterfactual Augmentation",
  "title_text": "Robust Estimation under Spurious Correlation via Statistical Counterfactual Augmentation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:38Z",
  "url": "https://research.draconitic.com/paper/ekjzpn73pjaaq",
  "venue": "arXiv",
  "versions": []
}