基于条件独立性检验的非稳态因果发现方法OA北大核心CSTPCD

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.