{"slug": "causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain", "title": "Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction", "summary": "Researchers propose Causal-Audit, a framework that enables large language models to perform explicit and auditable causal reasoning via target-aware causal graph construction and path-level evidence aggregation. The method outperforms existing LLM-based approaches on three benchmarks while providing interpretable reasoning traces.", "body_md": "arXiv:2607.15281v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain", "canonical_source": "https://arxiv.org/abs/2607.15281", "published_at": "2026-07-20 04:00:00+00:00", "updated_at": "2026-07-20 13:52:16.973608+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain", "markdown": "https://wpnews.pro/news/causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain.md", "text": "https://wpnews.pro/news/causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain.txt", "jsonld": "https://wpnews.pro/news/causal-audit-explicit-and-auditable-graph-based-reasoning-via-target-aware-chain.jsonld"}}