Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
This paper proposes Causal-Audit, a framework that formulates causal inference as structured reasoning over an explicit causal graph through four modular stages. Key innovations include a target-aware causal graph construction strategy that suppresses irrelevant variables and a path-level causal evidence aggregation mechanism that combines multiple paths. Experiments on three benchmarks show consistent outperformance over existing LLM methods while providing interpretable and auditable reasoning traces.
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[Submitted on 22 Apr 2026]
Title:Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
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Abstract:Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms. However, existing LLM-based methods often rely on implicit language-level reasoning, resulting in opaque causal assumptions, unverifiable reasoning paths, and fragile predictions under complex interventions, particularly in context-free settings. In this paper, we propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering. Our method formulates causal inference as structured reasoning over an explicit causal graph through four modular stages, rather than implicit end-to-end prediction. A key innovation is a target-aware causal graph construction strategy that treats the target variable as a core constraint during graph expansion, effectively suppressing irrelevant variables, spurious causal relations, and reasoning noise. We further introduce a path-level causal evidence aggregation mechanism that combines multiple causal paths while modeling both reinforcing and counteracting effects, enabling robust decision-making beyond single-chain reasoning. Extensive experiments on three benchmarks demonstrate that our framework consistently outperforms existing LLM-based methods while providing interpretable and auditable causal reasoning traces.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.15281 [cs.AI]
(or arXiv:2607.15281v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.15281
arXiv-issued DOI via DataCite
Journal reference: The 64th Annual Meeting of the Association for Computational Linguistics 2026
Submission history
From: Xuefei Yin [view email] [v1] Wed, 22 Apr 2026 23:24:18 UTC (201 KB)
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