Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
arXiv:2602.16481v1 Announce Type: new Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions. While expert knowledge is required to construct principled causal graphs, many statistical methods have been proposed to leverage observational data with varying formal guarantees. Causal Assumption-based Argumentation (ABA) is a framework that uses symbolic reasoning to ensure correspondence between input constraints and output graphs, while offering a principled way to combine data and expertise. We explore the use of large language models (LLMs) as imperfect experts for Causal ABA, eliciting semantic structural priors from variable names and descriptions and integrating them with conditional-independence evidence. Experiments on standard benchmarks and semantically grounded synthetic graphs demonstrate state-of-the-art performance, and we additionally introduce an evaluatio
arXiv:2602.16481v1 Announce Type: new Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions. While expert knowledge is required to construct principled causal graphs, many statistical methods have been proposed to leverage observational data with varying formal guarantees. Causal Assumption-based Argumentation (ABA) is a framework that uses symbolic reasoning to ensure correspondence between input constraints and output graphs, while offering a principled way to combine data and expertise. We explore the use of large language models (LLMs) as imperfect experts for Causal ABA, eliciting semantic structural priors from variable names and descriptions and integrating them with conditional-independence evidence. Experiments on standard benchmarks and semantically grounded synthetic graphs demonstrate state-of-the-art performance, and we additionally introduce an evaluation protocol to mitigate memorisation bias when assessing LLMs for causal discovery.
Executive Summary
This article explores the use of large language models (LLMs) in causal discovery, a crucial task in understanding cause-and-effect relationships from data. By integrating LLMs with causal assumption-based argumentation (ABA), the authors develop a novel approach to leveraging LLMs as imperfect experts for causal discovery. The proposed method, which elicits semantic structural priors from variable names and descriptions, demonstrates state-of-the-art performance on standard benchmarks and semantically grounded synthetic graphs. The authors also introduce an evaluation protocol to mitigate memorisation bias when assessing LLMs for causal discovery. This work has significant implications for the field of causal discovery and the potential applications of LLMs in various domains.
Key Points
- ▸ Causal discovery is a crucial task in understanding cause-and-effect relationships from data.
- ▸ The authors propose a novel approach to leveraging LLMs as imperfect experts for causal discovery using ABA.
- ▸ The method elicits semantic structural priors from variable names and descriptions and demonstrates state-of-the-art performance on standard benchmarks and semantically grounded synthetic graphs.
Merits
Strength in Leveraging LLMs
The proposed method successfully leverages LLMs, enabling the integration of data and expertise in causal discovery, which is a significant contribution to the field.
Improved Performance on Standard Benchmarks
The method demonstrates state-of-the-art performance on standard benchmarks and semantically grounded synthetic graphs, showcasing its effectiveness in causal discovery.
Demerits
Dependence on LLMs
The method's performance is heavily dependent on the quality and accuracy of the LLMs, which can be a limitation in certain scenarios where LLMs may not be reliable or available.
Potential Memorisation Bias
The evaluation protocol introduced by the authors to mitigate memorisation bias is a significant contribution, but it may still be a concern in certain cases where LLMs may memorise specific patterns in the data.
Expert Commentary
The proposed method is a significant contribution to the field of causal discovery, leveraging the power of LLMs to integrate data and expertise. The method's performance on standard benchmarks and semantically grounded synthetic graphs is impressive, and the evaluation protocol introduced by the authors to mitigate memorisation bias is a critical addition to the field. However, the method's dependence on LLMs and potential memorisation bias are limitations that need to be addressed. Overall, this work has significant implications for the field of causal discovery and the potential applications of LLMs in various domains.
Recommendations
- ✓ Future work should focus on addressing the method's dependence on LLMs and potential memorisation bias.
- ✓ The method should be further evaluated on a wider range of benchmarks and datasets to assess its robustness and generalisability.
