DCD：基于分解的从自相关和非平稳时间数据中因果发现
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
AI总结：
本文提出一种基于分解的因果发现方法，通过分离时间序列的趋势、季节性和残差成分，分别进行因果分析，从而在非平稳和自相关数据中更准确地恢复真实因果结构。
摘要：
多变量时间序列在金融、气候科学和医疗等领域中常表现出长期趋势、季节性模式和短期波动，这在非平稳性和自相关性下增加了因果推断的难度。现有因果发现方法通常基于原始观测，容易产生虚假边和误归因的时序依赖。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、季节性和残差成分，并对每个成分进行特定的因果分析。趋势成分使用平稳性检验，季节成分使用核方法依赖度量，残差成分使用基于约束的因果发现。所得的成分级图被整合为统一的多尺度因果结构。该方法隔离了长期和短期因果效应，减少了虚假关联，并提高了可解释性。在广泛的合成基准和真实世界气候数据上，我们的框架比最先进的基线方法更准确地恢复真实因果结构，特别是在强非平稳性和时间自相关性下。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
