CausalVerify: End-to-End Verification of Causal Analyses by Language Models
Language models increasingly perform empirical analyses end to end, yet existing evaluations assess the written explanation or whether generated code executes, not whether the executed workflow recovers the intended causal estimand. We introduce CausalVerify, an execution-grounded benchmark for end-to-end causal analysis that follows a model from research-context interpretation to estimand recovery. It scores this workflow at four distinct layers: method recognition, design specification, executable implementation, and estimand recovery. It combines 259 real-paper contexts, 100 fixed-seed synthetic scenarios with executable reference estimates, and 23 paper-twin pairs in which a model com...
| Authors | Yonghong Zhang, Ricardo Correia, Isabel M. Parra et al. |
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| Primary category | cs.AI |
| Published | 7 September 2026 |
| Last updated at source | 26 September 2026 |
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