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、气候科学和医疗等领域中，多变量时间序列常呈现长期趋势、季节模式及短期波动，这给非平稳性与自相关条件下的因果推断带来了挑战。现有因果发现方法通常直接处理原始观测值，易受虚假边和时序依赖误判的影响。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，并针对各分量进行特定因果分析：趋势分量采用平稳性检验，季节分量采用基于核的依赖度量，残差分量则采用基于约束的因果发现方法。由此获得的各分量级因果图被整合为统一的多尺度因果结构。该框架能分离长短程因果效应，减少虚假关联，并提升可解释性。在广泛的合成基准和真实气候数据测试中，我们的框架相比现有最优基线方法，尤其在高非平稳性和时序自相关条件下，能更准确地恢复真实因果结构。