提出新方法处理不规则时间序列的因果发现，效果远超传统方法。
Causal Discovery on Irregular Time Series
- 用预设时间窗聚合因果影响，替代固定时滞建模。
- 在不同信噪比下均准确恢复真实因果图，优于标准PCMCI+。
- 适合医疗、金融等不规则事件数据的因果分析场景。
因果发现方法在时间系统中表现优异，但通常依赖于规则离散的时滞结构，限制了其在定期采样数据外的应用。然而，许多现实任务需要处理不规则采样的事件流，如传感器数据、医疗记录和金融交易。本文提出对当前最先进的多变量规则时间序列因果发现方法PCMCI+的扩展，以支持不规则时间序列。该方法不再通过固定时滞依赖建模因果关系，而是将因果影响在预定义的时间窗口内进行聚合。我们在具有已知因果结构的合成不规则事件流上评估该方法，覆盖不同信噪比条件，结果表明其能持续准确恢复底层因果图，在不规则采样数据上的表现显著优于标准PCMCI+。
原文摘要 · Abstract (English)
Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions. In this work, we propose an extension of PCMCI+, a state-of-the-art method for causal discovery on regular multivariate time series, to allow for handling irregular time series. Instead of modelling causal relations through fixed-lag dependencies, our method aggregates causal influence over predefined temporal windows. We evaluate our method on synthetic irregular event streams with known causal structures under different signal-to-noise ratios, showing that it consistently recovers the underlying causal graph and substantially outperforms the standard PCMCI+ on irregularly sampled data.
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