Knowledge-Enhanced Causal Discovery Methods
发布时间：2026年09月17日
浏览次数：7
发布者: He Liu
主讲人： 顾丹蕾（数学中心）
活动时间： 从 2026-09-18 15:00 到 16:00
场地： 北京国际数学研究中心，镜春园78号院（怀新园）77201室
Causal discovery aims to identify causal relationships between variables from observational data; however, traditional data-driven methods often underutilize prior domain knowledge, facing challenges in both identifying causal relationships and interpreting underlying mechanisms within complex systems. This presentation introduces a knowledge-enhanced causal discovery method that integrates knowledge graphs and large language models into the discovery process. This approach generates candidate causal relationships and mechanistic hypotheses that are consistent with domain knowledge, subsequently employing data-driven methods for causal verification. By synergizing domain knowledge with observational data, this method offers a novel approach to uncovering causal mechanisms in complex systems.
