{
  "id": 5485337,
  "title": "A Computationally Feasible Framework for Causal Probabilistic Explanation",
  "url": "https://urgent.news/2026/09/03/a-computationally-feasible-framework-for-causal-probabilistic",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-03T17:55:43.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.04177v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis. Existing tools split into two camps. The theory of actual causality (AC) gives principled verdicts, but only for toy-sized models, because computing them requires enumerating counterfactual scenarios. Scalable attribution methods like SHAP (or…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}