Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification

• URL: http://arxiv.org/abs/2601.21210v1
• Date: Thu, 29 Jan 2026 03:22:58 GMT
• Title: Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
• Authors: Paul He, Yinya Huang, Mrinmaya Sachan, Zhijing Jin,
• Abstract summary: DoVerifier is a symbolic verifier that checks whether causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory.<n>Our evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces.
• Score: 56.51953062869371
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) are increasingly being applied to tasks that involve causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that do not capture whether the output of a model is formally valid under the semantics of causal reasoning. To address this, we propose DoVerifier, a simple symbolic verifier that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory. This allows us to recover correct answers to causal queries that would otherwise be marked incorrect due to superficial differences in their causal semantics. Our evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces, offering a more rigorous and informative way to evaluate LLMs on causal reasoning.

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