Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations

Excerpt

arXiv:2609.30867v1 Announce Type: new 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 s