分解时间序列成分，提升非平稳数据下的因果推断准确性
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
- 将时间序列拆分为趋势、季节和残差三部分分别分析
- 在真实气候数据上比现有方法更准识别因果结构
- 适合处理有长期趋势与周期性的复杂时序数据
金融、气候科学和医疗等领域中的多变量时间序列常包含长期趋势、季节性模式和短期波动，导致在非平稳性和自相关性下因果推断困难。现有方法通常基于原始观测，易产生虚假边和误判时间依赖。本文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，并分别采用平稳性检验、基于核的依赖度量和基于约束的因果发现进行分析。各分量的因果图整合为统一的多尺度因果结构。该方法能有效分离长短期因果效应，减少虚假关联，提升可解释性。在大量合成基准和真实气候数据上，本方法在强非平稳性和时间自相关条件下，均优于当前最优基线。
原文摘要 · Abstract (English)
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.
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