Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
Quick Answer
The Causal-Audit framework enhances causal reasoning in LLMs by using a target-aware causal graph construction, leading to more interpretable and robust decision-making.
Quick Take
Extensive experiments show it outperforms existing methods across three benchmarks, providing clearer causal reasoning paths.
Key Points
- Introduces a modular framework for context-free causal reasoning in LLMs.
- Employs target-aware causal graph construction to filter irrelevant variables.
- Implements path-level causal evidence aggregation for robust decision-making.
- Demonstrates superior performance over existing LLM methods on three benchmarks.
- Provides interpretable and auditable causal reasoning traces.
DeepSignal Analysis
What happened
The Causal-Audit framework introduces a structured approach to causal reasoning in large language models (LLMs) by utilizing a target-aware causal graph. This method aims to improve decision-making by providing clearer causal reasoning paths and reducing reliance on implicit reasoning methods.
Key evidence
- The proposed framework formulates causal inference through structured reasoning over an explicit causal graph, rather than relying on implicit end-to-end predictions.
- A target-aware causal graph construction strategy is introduced, which focuses on the target variable to minimize irrelevant variables and spurious causal relations.
- Extensive experiments on three benchmarks indicate that the Causal-Audit framework consistently outperforms existing LLM-based methods.
Why it matters
This framework addresses limitations in current LLMs, which often produce opaque reasoning and fragile predictions. By enhancing interpretability and robustness in decision-making, it could lead to more reliable applications of AI in critical areas requiring causal understanding.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.