计算机工程与应用2024，Vol.60Issue(10)：113-120,8.DOI:10.3778/j.issn.1002-8331.2301-0083
基于条件独立性检验的非稳态因果发现方法
Non-Stationary Causal Discovery Method Based on Conditional Independence Test
摘要
Abstract
Causal discovery on non-stationary time-series data is of great importance but challenging.Existing works mainly assume that the observed data change with time or domain,which requires the introduction of time or domain as prior knowledge.The aforementioned methods are usually unavailable on the segmented-stationary non-stationary scenarios.Therefore,this paper proposes a non-stationary causal discovery method that combines changepoint detection and structural vector auto-regressive model.It uses the changepoint detection method to identify the time point of change,then divides the time of the previous step into stationary intervals,and further uses the stationary algorithm to infer their local causal structures.Experiments on simulated and real-world data prove the effectiveness of the proposed method.关键词
因果关系发现/非稳态/因果网络Key words
causal discovery/non-stationary/causal network分类
信息技术与安全科学引用本文复制引用
郝志峰,张维杰,蔡瑞初,陈薇..基于条件独立性检验的非稳态因果发现方法[J].计算机工程与应用,2024,60(10):113-120,8.基金项目
科技创新2030-"新一代人工智能"重大项目(2021ZD0111501) （2021ZD0111501）
国家优秀青年科学基金(62122022) （62122022）
国家自然科学基金(61876043,61976052,62206064). （61876043,61976052,62206064）
