{
  "abstract": "Estimating causal effects with missing outcomes requires learning outcome, exposure, and selection models. Flexible propensity learners can predict treatment or missingness well while worsening target estimation by emphasizing instruments, weak-overlap regions, or variation unrelated to outcome-regression bias. We propose a collaborative representation-based targeted learning method for recovered average treatment effects under outcome selection. The method learns low-dimensional exposure and selection representations from cross-fitted pseudo-outcomes that encode outcome-regression drift. A finite candidate library alternates targeted outcome updates with representation updates, and inner cross-validation selects representation complexity, collaboration strength, and regularization using a target-aware risk. We give high-level sufficient conditions for collaborative robustness, consistency, and asymptotic linearity after outer validation-fold targeting. Simulations show finite-sample bias reduction, with explicit bias-variance trade-offs across configurations and relative to competing estimators",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Machine Learning and Knowledge Discovery in Databases. Research Track, ECML PKDD 2026, Lecture Notes in Computer Science, vol. 16941, pp. 623-640, Springer, 2027",
  "arxiv": "2610.08073",
  "author_total": 1,
  "authors": [
    {
      "name": "Johan de Aguas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0c5exfcnzwwdx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08073",
  "fulltext_url": null,
  "id": "0c5exfcnzwwdx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08073",
  "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/2610.08073",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0c5exfcnzwwdx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "2610.08073",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08073"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Collaborative representations for targeted causal inference under outcome selection",
  "title_text": "Collaborative representations for targeted causal inference under outcome selection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/0c5exfcnzwwdx",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1007/978-3-032-37654-1_36",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}