Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
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Computer Science > Computation and Language
Title:Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
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: this https URL
| Comments: | Accepted at ClimateNLP 2026, the 3rd Workshop on Natural Language Processing meets Climate Change (EMNLP 2026). 9 pages plus appendix (21 pages total), 6 figures, 15 tables |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2609.30867 [cs.CL] |
| (or arXiv:2609.30867v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.30867
arXiv-issued DOI via DataCite (pending registration)
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