{
  "abstract": "AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified. A generated feature may represent a treatment version, pre-action state, history, design variable, mediator, outcome proxy, observation process, or intercurrent event; these roles are not interchangeable. We formulate a causal type discipline for sequential experiments: a versioned representation map, a causal role classifier, a claim-status filter, and an estimand lock. The lock fixes a standardized proximal effect before generated covariates enter the analysis. Under audit correctness and standard identification assumptions, admissible role assignments preserve this estimand. We apply the established conditional-covariance characterization of compression bias to substitution of generated representations for design-relevant states. A standardized decomposition separates compression, conditional-law, and standardization drift. Further results cover mediator adjustment, post-action leakage, marker-intervention conflation, outcome-guided discovery, and state-measurement error. Cluster-level orthogonal estimators distinguish empirical and superpopulation targets under repeated sessions and missing outcomes. Simulations show that refinement helps when it retains design-relevant information, whereas design erasure, leakage, and same-data marker selection can produce bias or undercoverage. The framework places causal semantics and claim status before confirmatory inference with generated representations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17772",
  "author_total": 2,
  "authors": [
    {
      "name": "Takes Fujita"
    },
    {
      "name": "Nobutaka Hattori"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3wq1z7ypc1qms.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17772",
  "fulltext_url": null,
  "id": "3wq1z7ypc1qms",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17772",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Advanced Causal Inference Techniques"
    ]
  ],
  "openalex": "W7213527305",
  "pdf_url": "https://arxiv.org/pdf/2609.17772",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3wq1z7ypc1qms.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "2609.17772",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17772"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments",
  "title_text": "When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:29Z",
  "url": "https://research.draconitic.com/paper/3wq1z7ypc1qms",
  "venue": "arXiv",
  "versions": []
}