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From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data (2306.16902v1)

Published 29 Jun 2023 in cs.AI

Abstract: LLMs exhibit exceptional abilities for causal analysis between concepts in numerous societally impactful domains, including medicine, science, and law. Recent research on LLM performance in various causal discovery and inference tasks has given rise to a new ladder in the classical three-stage framework of causality. In this paper, we advance the current research of LLM-driven causal discovery by proposing a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning. To make LLM more than a query tool and to leverage its power in discovering natural and new laws of causality, we integrate the valuable LLM expertise on existing causal mechanisms into statistical analysis of objective data to build a novel and practical baseline for causal structure learning. We introduce a universal set of prompts designed to extract causal graphs from given variables and assess the influence of LLM prior causality on recovering causal structures from data. We demonstrate the significant enhancement of LLM expertise on the quality of recovered causal structures from data, while also identifying critical challenges and issues, along with potential approaches to address them. As a pioneering study, this paper aims to emphasize the new frontier that LLMs are opening for classical causal discovery and inference, and to encourage the widespread adoption of LLM capabilities in data-driven causal analysis.

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Authors (4)
  1. Taiyu Ban (6 papers)
  2. Lyvzhou Chen (1 paper)
  3. Xiangyu Wang (79 papers)
  4. Huanhuan Chen (42 papers)
Citations (47)
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Summary

Analyzing the Integration of LLMs with Causal structure Learning

The paper, "From Query Tools to Causal Architects: Harnessing LLMs for Advanced Causal Discovery from Data," presents an innovative framework for enhancing causal discovery by incorporating LLMs within traditional data-driven approaches. The authors propose a methodology that leverages LLM-driven causal insights alongside observed data to improve causal structure learning (CSL). This synthesis opens new avenues in causal discovery and sets the groundwork for potentially more effective and reliable identification of causal structures.

Traditionally, CSL is a complex task, particularly due to its NP-hard nature, which manifests in significant challenges when dealing with large-scale datasets and constraints inherent to real-world data. These challenges lead to sub-optimal recovery of Bayesian Networks (BNs) that model causal relationships. Prior research has largely relied on data alone or expert input, both of which have limitations in scalability and accuracy. The authors address these issues by employing LLMs as rich sources of causal knowledge that complement the statistical insights derived from data.

A key innovation in the paper is the development of a universal set of prompts designed to maximize LLMs' outputs concerning direct causal relationships between variables. This set of prompts helps to encode the collective knowledge embodied in LLMs into statistical models, which facilitates higher quality learning of causal structures from data. The structured approach of using prompts effectively transforms LLMs from passive query tools into active components of the causal discovery process.

The paper details two primary methodologies for integrating LLM-driven causal knowledge into CSL: the hard constraint-based approach and the soft constraint-based approach. The hard constraint method prunes inconsistent Bayesian Networks (BNs), optimizing the search for BNs within a constraint-satisfied space. Meanwhile, the soft constraint approach modifies the scoring function to incorporate prior knowledge flexibly, tolerating potential errors. Both approaches aim to enhance the reliability and accuracy of the causal structures learned from data.

Empirical validation of the proposed framework on eight benchmark datasets shows significant improvement in the accuracy of recovered causal structures when incorporating LLM-derived prior knowledge. Notably, GPT-4 demonstrated strong performance, especially in domains with smaller scale causal structures, whereas its efficacy diminished in domains with more complex causal interactions. The empirical results highlight the potential of using LLMs to refine the CSL process, contributing to better model accuracy and reliability.

The paper also discusses the limitations of current LLMs like GPT-4 in generating qualitatively accurate causal statements. Errors in causal inference, such as incorrect reasoning and temporal disregard, are highlighted as areas requiring further development. These insights suggest that while the proposed framework marks a significant advancement, there is room for improving LLMs' understanding of causality and direct causality identification.

The implications of this research are profound, offering a new direction for CSL by integrating LLMs. By treating LLM-derived causal knowledge as input, the paper sets the stage for the development of more robust CSL algorithms. Future research may focus on refining LLM prompt strategies to extract higher quality causal knowledge, investigating other forms of prior constraints that LLMs can generate, and improving CSL algorithms' ability to differentiate between valid and erroneous prior constraints.

Overall, the paper provides a compelling blueprint for integrating LLMs with CSL, offering promising results and raising important questions for further exploration in the field of causal discovery and artificial intelligence.

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