CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
Automating theoretical research requires generating candidate results and evaluating them reliably. Models keep getting better at the first, while the second remains hard. A common approach asks one large language model (LLM) to review what another produced, yet such reviewers are empirically unreliable: they may accept fabricated papers and catch the fabrication at close to chance rates~\citep{badscientist2025}. We present \textsc{CausalSmith}, a framework for automated theoretical research in causal inference built on the Lean proof assistant, where a proof is checked by a program rather than read by a referee. \textsc{CausalSmith} rests on \textsc{Causalean}, a foundational Lean librar...
| Authors | Jiyuan Tan, Vasilis Syrgkanis |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 15 September 2026 |
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