arXivPaperNeeds Review
CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
CausalForge pairs a Lean-verified causal-inference library with an autonomous research pipeline and a semantic statement audit. Formal proof checks derivation, not whether the theorem matches the intended claim.
arXiv · Jul 24, 2026
Source Summary
CausalForge combines **7,035 machine-checked declarations** in the Causalean Lean library with CausalSmith, an agent pipeline that selects topics, proposes results, formalizes statements, builds proofs, and presents artifacts for review.
Practical Implication
Builders of research agents should separate mechanical validity from semantic validity. The framework uses Lean for proof checking and a **statement audit** to compare each formal theorem with the informal scientific claim it is meant to encode.
Agent-Ready Context
CausalForge combines **7,035 machine-checked declarations** in the Causalean Lean library with CausalSmith, an agent pipeline that selects topics, proposes results, formalizes statements, builds proofs, and presents artifacts for review. Builders of research agents should separate mechanical validity from semantic validity. The framework uses Lean for proof checking and a **statement audit** to compare each formal theorem with the informal scientific claim it is meant to encode. A kernel-checked proof establishes only that a statement follows from its assumptions. It cannot establish that the formalization captures the right scientific question, so human inspection remains part of the presented workflow.
Context Map
agentresearchdata#harness-engineering#agent-reliability#reasoningUncertainty
A kernel-checked proof establishes only that a statement follows from its assumptions. It cannot establish that the formalization captures the right scientific question, so human inspection remains part of the presented workflow.