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Large Language Models for Causal Discovery: Current Landscape and Future Directions
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Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently. While CD specializes in uncovering cause-effect relationships from data, and LLMs excel at natural language processing and generation, their integration presents unique opportunities for advancing causal understanding. This survey examines how LLMs are transforming CD across three key dimensions: direct causal extraction from text, integration of domain knowledge into statistical methods, and refinement of causal structures. We systematically analyze approaches that leverage LLMs for CD tasks, highlighting their innovative use of metadata and natural language for causal inference. Our analysis reveals both LLMs' potential to enhance traditional CD methods and their current limitations as imperfect expert systems. We identify key research gaps, outline evaluation frameworks and benchmarks for LLM-based causal discovery, and advocate future research efforts for leveraging LLMs in causality research. As the first comprehensive examination of the synergy between LLMs and CD, this work lays the groundwork for future advances in the field.
Forward citations
Cited by 4 Pith papers
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KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI
A production RCA system combining API-level drilldown, a skeleton-based causal graph prior, and memory-augmented multi-agent LLM reasoning, reporting 0.88 AC@1 root-cause localization on 300 Kuaishou failures.
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KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI
A deployed RCA system using API-level drilldown, a skeleton causal graph, and memory-augmented multi-agent LLM reasoning localizes root-cause services and failure types with AC@1 of 0.88/0.79 in a 200k-service product...
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When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
Introduces EPC-AW to mitigate epistemic miscalibration in LLM multi-agent planning via consistency-based selection and refinement, reporting 9.75% average success improvement.
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KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI
KRCA uses API-level drilldown, skeleton causal graphs from anomalous metrics, and memory-augmented multi-agents to reach AC@1 of 0.88 for root cause localization and 0.79 for failure classification in hyper-scale micr...
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