Title: Evolving DAGs with LLM: Towards Smart and Hallucination-Mitigated Causal Discovery
Primary area: causal reasoning
Keywords: Casual Discovery, Causality, Large Language Model, Evolutionary Algorithm, Directed Acyclic Graph

Abstract: Causal discovery seeks to infer causal Directed Acyclic Graphs (DAGs) from observational data. To optimize the fitness between a candidate DAG and the observational data, deep learning and heuristic approaches have been widely adopted. However, both paradigms rely solely on data, influenced by properties such as scale, sparsity, and distribution complexity, which often leads to performance bottleneck in complex causal DAGs. To overcome this bottleneck, a promising alternative is to incorporate the semantic information inherent in variables and further exploit the external world knowledge encoded in large language models (LLMs). In this paper, we propose Evolving DAG with LLM (EvoDAG-LM), a novel LLM-assisted evolutionary algorithm for causal discovery, where EvoDAG-LM integrates LLMs into three key phases of the evolutionary process, i.e., search space construction, evolutionary operation enhancement, and loop elimination. This enables the intelligent heuristic search of DAGs. Incorporating LLMs does not imply unconditional trust in it; unlike prior approaches that let LLMs directly determine causal edges, EvoDAG-LM confines LLMs to auxiliary roles and grounds all final decisions in statistical scoring, thereby effectively mitigating hallucination risks. Additionally, to fully yet cautiously exploit the potential of LLMs, EvoDAG-LM adopts advanced prompting strategies and a scale-aware adaptation mechanism to selectively invoke LLM assistance. Experiments demonstrate the superiority and scalability of EvoDAG-LM compared to diverse state-of-the-art methods.