{
  "id": 14526,
  "url": "https://arxiv.org/abs/2502.10605",
  "title": "Optimal Causal Annotations: An Application to Casenotes in Social Services",
  "summary": "arXiv:2502.10605v4 Announce Type: replace-cross Abstract: Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain. LLMs can provide text annotation at scale but may be subject to unknown bias. When ground-truth outcomes require expensive expert labeling or follow-up, budget limits typically allow only a fraction of the data to be labeled. Motivated by collaboration with a nonprofit conducting stree",
  "authors": "Ezinne Nwankwo, Lauri Goldkind, Angela Zhou",
  "category": "research",
  "topics": "bias-fairness,regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T04:00:00.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14526",
  "original_url": "https://arxiv.org/abs/2502.10605",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}