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.

翻译：本研究提出了一种新颖的基于约束的因果发现方法，用于处理自相关与非平稳时间序列数据（CDANs）。所提方法解决了现有自相关与非平稳时间序列因果发现方法的多项局限性，例如高维性问题、无法识别滞后因果关系以及忽略变化模块的问题。我们的方法既可识别滞后及即时/同期因果关系，又能检测随时间动态变化的变化模块。该方法在约束搜索中通过考虑滞后父节点而非基于整个过去进行条件设置，从而优化了条件集，有效应对高维性挑战；同时通过同时考虑同期与滞后父节点来检测变化模块。本方法首先检测滞后邻接关系，继而识别变化模块与同期邻接关系，最终确定因果方向。我们利用合成数据集与真实世界临床数据集对所提方法进行了全面评估，并与多种基线方法进行了性能对比。实验结果表明，该方法在检测自相关与非平稳时间序列数据中的因果关系及变化模块方面具有显著有效性。