ALCM: Autonomous LLM-Augmented Causal Discovery Framework

2024/05/02 by Elahe Khatibi, Khatibi, Elahe, Mahyar Abbasian +7 · 10 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Methodology (stat.ME) #Risk and Safety Analysis

paper · pdf · doi:10.48550/arxiv.2405.01744

openalex publication_date 2024/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Robust Causal Discovery under Imperfect Structural Constraints
• Bayesian Network Structure Discovery Using Large Language Models
• Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
• FinCARE: Financial Causal Analysis with Reasoning and Evidence
• Improving constraint-based discovery with robust propagation and reliable LLM priors
• Revealing Multimodal Causality with Large Language Models
• Evaluation of Causal Reasoning for Large Language Models in Contextualized Clinical Scenarios of Laboratory Test Interpretation
• GRID: Graph-based Reasoning for Intervention and Discovery in Built Environments
• ORCA: ORchestrating Causal Agent
• Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach

Related

• Integrating Large Language Model for Improved Causal Discovery
• Causal Structure Learning Supervised by Large Language Model
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
• Large Language Models for Causal Discovery: Current Landscape and Future Directions
• CARE: Turning LLMs Into Causal Reasoning Expert