{
  "abstract": "Digital twins are increasingly used to ask how a patient's health would evolve under alternative treatment strategies. When a twin couples a mechanistic model with learned components and is fitted to observational records, its answer mixes information from the data with assumptions from the mechanism. For sustained strategies that begin at a clinical landmark, we split the counterfactual mean into a part the data support, where patients follow the strategy with probability bounded away from zero, and a part they do not. On the supported part, a cross-fitted sequential doubly robust estimator that uses the twin as its outcome model is consistent whenever the decision model is, so twins that fit equally well but disagree as plug-in estimators agree after correction. On the unsupported part, we characterize the values compatible with the data and with a residual of size at most epsilon around the mechanism. This set is nested in epsilon, reduces to a point when the mechanism is exact and identifiable, and recovers assumption-free bounds as epsilon grows, so the penalty weight of a physics-informed loss becomes a sensitivity parameter. Knowledge constraints on how covariates enter the mechanism shrink the set, and when support grows over time, back-testing gives a data-based lower bound on epsilon. In simulations based on an energy-balance model of weight change under glucagon-like peptide-1 receptor agonist therapy, the correction reduced the spread of estimates across four twins from 0.66 to 0.03 percentage points of weight loss with conservative coverage; on unsupported patients, the exact mechanism was rejected in every replicate, the bounds covered the truth at every residual size not rejected, and a mechanism-free extrapolation was biased by 0.5 points.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.31706",
  "author_total": 1,
  "authors": [
    {
      "name": "Cong Cao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8dchnqyebhcx6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.31706",
  "fulltext_url": null,
  "id": "8dchnqyebhcx6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.31706",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2610.31706",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8dchnqyebhcx6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:25Z",
      "native_id": "2610.31706",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.31706"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Where the Data End and the Mechanism Begins: Doubly Robust Causal Digital Twins for Treatment Strategies",
  "title_text": "Where the Data End and the Mechanism Begins: Doubly Robust Causal Digital Twins for Treatment Strategies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:25Z",
  "url": "https://research.draconitic.com/paper/8dchnqyebhcx6",
  "venue": "arXiv",
  "versions": []
}