Mitigating Causal Bias in LLMs via Potential Outcomes Framework and Actual Causality Theory
Year: 2026
Type: conference-paper
Abstract: Event Causality Identification (ECI) aims to identify causal relationships between events, which is essential for root cause analysis.While recent studies reveal that Large Language Models (LLMs) exhibit significant causal hallucination, a systematic evaluation of their document-level ECI performance across varied structural characteristics and a corresponding dataset is currently lacking.To fill this gap, we first construct a structurecontrolled dataset to comprehensively assess their document-level ECI performance across texts with various structural characteristics that influence the causal behaviors in ECI.We find that different LLMs exhibit divergent causal bias across texts with varied structures, ranging from consistent hallucination or neglect to structure-dependent shifts between the two.To mitigate the bias, furthermore, we formulate ECI as a causal inference problem and propose a causality identification framework grounded in the potential outcomes and the Halpern-Pearl (HP) definition of actual causality theory.Experimental results demonstrate that our framework significantly reduces the causal bias associated with directly using LLMs on ECI, while also achieving superior performance.
Authors: Yiheng Zhao, Yuanliang Li, Shreya Savant, Jun Yan
Institution: Concordia University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140076111