This study presents a novel constraint-based causal discovery approach for
autocorrelated and non-stationary time series data (CDANs). Our proposed method
addresses several limitations of existing causal discovery methods for
autocorrelated and non-stationary time series data, such as high
dimensionality, the inability to identify lagged causal relationships, and the
overlook of changing modules. Our approach identifies both lagged and
instantaneous/contemporaneous causal relationships along with changing modules
that vary over time. The method optimizes the conditioning sets in a
constraint-based search by considering lagged parents instead of conditioning
on the entire past that addresses high dimensionality. The changing modules are
detected by considering both contemporaneous and lagged parents. The approach
first detects the lagged adjacencies, then identifies the changing modules and
contemporaneous adjacencies, and finally determines the causal direction. We
extensively evaluated the proposed method using synthetic datasets and a
real-world clinical dataset and compared its performance with several baseline
approaches. The results demonstrate the effectiveness of the proposed method in
detecting causal relationships and changing modules in autocorrelated and
non-stationary time series data.

该研究提出了一种新颖的基于约束条件的因果发现方法，用于自相关和非平稳时间序列数据中的模块变化检测和因果关系识别。