CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference

LLM-driven automated research is limited by unreliable evaluation, since LLM reviewers can accept fabricated results near chance levels. CausalForge addresses this by grounding causal inference research in the Lean proof assistant, pairing a machine-checked causal inference library (Causalean) with an agentic pipeline (CausalSmith) that proposes, formalizes, and proves results, then audits whether formal statements faithfully represent the intended informal claims. This offers a template for trustworthy automated scientific discovery, with applications in mathematics and causal inference research acceleration, verifiable AI-assisted theorem generation, and building reviewer-independent quality assurance into autonomous research systems. Authors: Jiyuan Tan, Vasilis Syrgkanis Paper: https://arxiv.org/abs/2607.22511v1