Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing causal discovery methods typically operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies. We introduce a decomposition-based causal discovery framework that separates each time series into trend, seasonal, and residual components and performs component-specific causal analysis. Trend components are assessed using stationarity tests, seasonal components using kernel-based dependence measures, and residual components using constraint-based causal discovery. The resulting component-level graphs are integrated into a unified multi-scale causal structure. This approach isolates long- and short-range causal effects, reduces spurious associations, and improves interpretability. Across extensive synthetic benchmarks and real-world climate data, our framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, particularly under strong non-stationarity and temporal autocorrelation.

翻译：在金融、气候科学和医疗健康等领域，多元时间序列常呈现长期趋势、季节模式与短期波动，使得非平稳性和自相关条件下的因果推断变得复杂。现有因果发现方法通常直接处理原始观测值，易受伪边和错误归因的时间依赖性影响。我们提出一种基于分解的因果发现框架，将每个时间序列分解为趋势、季节和残差分量，并执行分量特异的因果分析。趋势分量通过平稳性检验评估，季节分量采用基于核的依赖度量，残差分量则使用基于约束的因果发现方法。所得分量级图被整合为统一的多尺度因果结构。该方法能够分离长程与短程因果效应，减少伪关联并提升可解释性。在大量合成基准测试和真实气候数据上的实验表明，相较于现有先进基线方法，我们的框架能更准确地还原真实因果结构，尤其在强非平稳性和时间自相关条件下表现突出。