Causal Structure Learning Supervised by Large Language Model
(2311.11689)Abstract
Causal discovery from observational data is pivotal for deciphering complex relationships. Causal Structure Learning (CSL), which focuses on deriving causal Directed Acyclic Graphs (DAGs) from data, faces challenges due to vast DAG spaces and data sparsity. The integration of LLMs, recognized for their causal reasoning capabilities, offers a promising direction to enhance CSL by infusing it with knowledge-based causal inferences. However, existing approaches utilizing LLMs for CSL have encountered issues, including unreliable constraints from imperfect LLM inferences and the computational intensity of full pairwise variable analyses. In response, we introduce the Iterative LLM Supervised CSL (ILS-CSL) framework. ILS-CSL innovatively integrates LLM-based causal inference with CSL in an iterative process, refining the causal DAG using feedback from LLMs. This method not only utilizes LLM resources more efficiently but also generates more robust and high-quality structural constraints compared to previous methodologies. Our comprehensive evaluation across eight real-world datasets demonstrates ILS-CSL's superior performance, setting a new standard in CSL efficacy and showcasing its potential to significantly advance the field of causal discovery. The codes are available at \url{https://github.com/tyMadara/ILS-CSL}.
Overview
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The paper introduces an Iterative LLM Supervised Causal Structure Learning (ILS-CSL) framework, enhancing the process of uncovering causal relationships using LLMs.
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ILS-CSL utilizes LLMs to refine the quality of casual structure learning (CSL) by iteratively merging LLM-based causal inferences into the CSL process.
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The framework has been evaluated on eight real-world datasets, showing significant improvements in CSL, especially as dataset complexity increases.
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ILS-CSL demonstrates reduced erroneous constraints, decreased computational overhead, and sets a new benchmark in utilizing LLM for causal discovery in complex systems.
Iterative LLM Supervised Causal Structure Learning Framework (ILS-CSL)
Introduction to Causal Structure Learning
Causal Structure Learning (CSL) is dedicated to uncovering the causal relationships among variables within a dataset, typically represented by a Directed Acyclic Graph (DAG). Despite the significance of CSL in various fields for understanding complex systems, it is challenged by the combinatorial explosion in DAGs' space and often sparse and noisy observational data. To tackle these challenges, the integration of prior knowledge, especially incorporating inference capabilities of LLMs, into CSL presents a novel direction. LLMs' adeptness at causal reasoning can potentially refine the quality of CSL by providing knowledge-based causal inferences.
The ILS-CSL Framework
The Iterative LLM Supervised CSL (ILS-CSL) framework stands as a progressive method to merge LLM-based causal inferences directly into the CSL process. Unlike previous works that either utilize LLM-derived constraints separately or apply them uniformly across all variable pairs, ILS-CSL optimally focuses on validating and refining causal links suggested by CSL iteratively. This iterative feedback cycle between LLM inferences and CSL refinement ensures a potent use of LLM resources, yielding robust structural constraints and enhancing CSL efficacy.
Performance Evaluation and Contributions
Evaluated comprehensively across eight real-world datasets, ILS-CSL has demonstrated considerable improvements in data-driven CSL, particularly excelling as the complexity of the dataset grows. The framework is advantageous in that it:
- Produces powerful structural constraints by transforming LLM causal inferences into direct edge information or absence thereof in the DAG.
- Considerably mitigates the errors from imperfect LLM inferences, theoretically reducing erroneous constraints by a factor related to the variables' count.
- Significantly decreases the computational overhead for LLM inferences, approximating the pairwise variable inferences from a quadratic to a linear factor concerning the number of variables.
Theoretical Implications and Future Directions
The adoption of ILS-CSL marks a strategic utilization of LLMs in enhancing CSL, overcoming previous methodologies' limitations concerning scalability and reliability of LLM-derived constraints. The significant reduction in erroneous constraints and computational efficiency paves the way for applying this framework in more extensive and complex real-world scenarios. Future work could explore the integration of ILS-CSL with various CSL algorithms and further refine the framework's efficiency in managing LLM resources and mitigating imperfections in LLM inferences.
Concluding Remarks
ILS-CSL establishes a new benchmark in the integration of LLM inferences for causal discovery, showcasing substantial enhancements over traditional CSL methodologies and previous LLM-based approaches. The framework's iterative refinement process ensures that the maximum potential of LLMs is harnessed, heralding a promising direction for advancing causal discovery in complex systems.