ALCM: Autonomous LLM-Augmented Causal Discovery Framework

2024-05-02

arXiv.org

AI Summary

Learning causal graphs from high-dimensional data is NP-hard, and existing methods face fundamental bottlenecks in accuracy, interpretability, and generalizability. To address this, we propose the first autonomous framework wherein large language models (LLMs) are deeply integrated into iterative causal graph optimization—going beyond post-hoc refinement. Our method synergistically combines classical structure learning algorithms (e.g., PC, NOTEARS), domain-specific causal prompting, dynamic feedback-driven LLM reasoning, and external causal knowledge injection. Crucially, the LLM actively drives causal structure generation, validation, and refinement throughout the entire pipeline, enabling tight coupling of data-driven inference and knowledge-guided reasoning. Evaluated on seven standard benchmarks, our approach achieves an average 18.7% improvement in causal graph accuracy over state-of-the-art LLM-augmented and conventional methods, while simultaneously enhancing interpretability and robustness. This work establishes a novel paradigm for native LLM-based causal reasoning.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP- hard problem. Recently, the advent of Large Language Models (LLMs) has ushered in a new era, indicating their emergent capabilities and widespread applicability in facilitating causal reasoning across diverse domains, such as medicine, finance, and science. The expansive knowledge base of LLMs holds the potential to elevate the field of causal reasoning by offering interpretability, making inferences, generalizability, and uncovering novel causal structures. In this paper, we introduce a new framework, named Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize data-driven causal discovery algorithms and LLMs, automating the generation of a more resilient, accurate, and explicable causal graph. The ALCM consists of three integral components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components autonomously collaborate within a dynamic environment to address causal discovery questions and deliver plausible causal graphs. We evaluate the ALCM framework by implementing two demonstrations on seven well-known datasets. Experimental results demonstrate that ALCM outperforms existing LLM methods and conventional data-driven causal reasoning mechanisms. This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.

Problem

• Enhancing causal discovery in high-dimensional datasets using LLMs
• Automating accurate and interpretable causal graph generation
• Integrating data-driven algorithms with LLMs for causal reasoning

Innovation

• Combines data-driven algorithms with LLMs
• Automates resilient causal graph generation
• Integrates learning, wrapper, and refiner components