@inproceedings{shen-etal-2025-exploring,
title = "Exploring Multi-Modal Data with Tool-Augmented {LLM} Agents for Precise Causal Discovery",
author = "Shen, ChengAo and
Chen, Zhengzhang and
Luo, Dongsheng and
Xu, Dongkuan and
Chen, Haifeng and
Ni, Jingchao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.36/",
doi = "10.18653/v1/2025.findings-acl.36",
pages = "636--660",
ISBN = "979-8-89176-256-5",
abstract = "Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. The advent of Large Language Models (LLMs) has ushered in an affordable way of leveraging the semantic cues for knowledge-driven causal discovery, but the development of LLMs for causal discovery lags behind other areas, particularly in the exploration of multi-modal data. To bridge the gap, we introduce MatMCD, a multi-agent system powered by tool-augmented LLMs. MatMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. The proposed design of the inner-workings ensures successful cooperation of the agents. Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery."
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%0 Conference Proceedings
%T Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
%A Shen, ChengAo
%A Chen, Zhengzhang
%A Luo, Dongsheng
%A Xu, Dongkuan
%A Chen, Haifeng
%A Ni, Jingchao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F shen-etal-2025-exploring
%X Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery methods, while well-established, predominantly rely on observational data and often overlook the semantic cues inherent in cause-and-effect relationships. The advent of Large Language Models (LLMs) has ushered in an affordable way of leveraging the semantic cues for knowledge-driven causal discovery, but the development of LLMs for causal discovery lags behind other areas, particularly in the exploration of multi-modal data. To bridge the gap, we introduce MatMCD, a multi-agent system powered by tool-augmented LLMs. MatMCD has two key agents: a Data Augmentation agent that retrieves and processes modality-augmented data, and a Causal Constraint agent that integrates multi-modal data for knowledge-driven reasoning. The proposed design of the inner-workings ensures successful cooperation of the agents. Our empirical study across seven datasets suggests the significant potential of multi-modality enhanced causal discovery.
%R 10.18653/v1/2025.findings-acl.36
%U https://aclanthology.org/2025.findings-acl.36/
%U https://doi.org/10.18653/v1/2025.findings-acl.36
%P 636-660
Markdown (Informal)
[Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery](https://aclanthology.org/2025.findings-acl.36/) (Shen et al., Findings 2025)
ACL