CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
CDANs：自相关与非平稳时间序列中的时序因果发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究观测时间序列中的因果关系发现，并处理自相关与非平稳性。
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CDANs：自相关与非平稳时间序列中的时序因果发现
- 研究问题
- 研究自相关且非平稳的观测时间序列中，如何发现滞后和同期因果关系，并识别随时间变化的机制，同时缓解高维搜索困难。
- 方法与思路
- 作者提出基于约束的搜索，利用滞后父节点优化条件集，依次识别滞后邻接、变化机制和同期邻接，最后确定因果方向。
- 材料陈述的贡献
- 摘要称方法可同时发现滞后、同期关系及变化机制，并在合成数据和临床数据中与多个基准比较，实验显示方法有效。
- 阅读关注点
- 应核对因果方向识别和条件独立检验所需假设、非平稳机制的定义，以及潜在混杂、滞后阶数和高维条件集的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明识别假设、理论保证及量化结果，实验表现不能直接验证因果关系。
原文摘要
Time series data are found in many areas of healthcare such as medical time series, electronic health records (EHR), measurements of vitals, and wearable devices. Causal discovery, which involves estimating causal relationships from observational data, holds the potential to play a significant role in extracting actionable insights about human health. In this study, we present 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 overlooking changing modules. Our approach identifies 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 our proposed method on synthetic and real-world clinical datasets, and compared its performance with several baseline approaches. The experimental results demonstrate the effectiveness of the proposed method in detecting causal relationships and changing modules for autocorrelated and non-stationary time series data.