| title | Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations |
|---|---|
| authors | Yonghong Zhang, Yong Xie, Isabel M. Parra, Ricardo Correia |
| date | 2026-09-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.30867 |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Difference-in-differences (DID) studies are widely used to evaluate climate policy, but assessing the evidence supporting their identification assumptions remains challenging. We introduce ARGUS, a structured language-model pipeline that audits reported evidence against an eleven-dimension assumption-implication-evidence rubric and abstains when relevant evidence cannot be retrieved. We evaluate ARGUS using injected flaws, economics papers, and a small pilot with reconciled labels. On the 11-flaw benchmark, ARGUS detects 73% of planted flaws, compared with 18% for a keyword-based pipeline. Across 26 economics papers, ARGUS abstains on about 40% of paper-dimension assessments for lack of retrievable evidence. In a five-paper pilot with labels reconciled by two annotators, it assigns a higher risk level than the labels on 25 of the 33 assessments it completes. A rule fixed before the labels arrived removes most of this in-sample; weighted agreement stays low. ARGUS provides evidence-linked risk reports that localize potential weaknesses for expert review, without adjudicating causal claims. Code and data: https://github.com/yonghongzhang-io/ARGUS |
cite
@misc{zhang2026evidence,
title={Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations},
author={Yonghong Zhang and Yong Xie and Isabel M. Parra and Ricardo Correia},
year={2026},
eprint={2609.30867},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.30867}
}