{
  "id": 7149,
  "url": "https://arxiv.org/abs/2603.15926v1",
  "title": "Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare",
  "summary": "Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical records data. We evaluate the Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference algorithms on structural recovery and path-specific fairness decomposition, going beyond composite fairness scores. On synthetic data, Peter-Cl",
  "authors": "Nitish Nagesh, Elahe Khatibi, Thomas Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-16T21:19:17.000Z",
  "fetched_at": "2026-07-14T16:32:59.167Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7149",
  "original_url": "https://arxiv.org/abs/2603.15926v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}