动态稀疏注意力网络提升多变量时间序列因果发现的精度与可解释性
Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in Multivariate Time Series
- 融合膨胀卷积与动态稀疏注意力，自适应过滤虚假连接
- 在金融和营销数据上显著降低误发现率，精准捕捉因果延迟
- 热力图可解释性强，适合需洞察隐藏因果机制的场景
在金融、营销等领域的多变量时间序列中，理解因果关系对有效决策至关重要。现有方法难以应对复杂依赖与滞后效应。本文提出 DyCAST-Net，通过膨胀卷积捕捉多尺度时序依赖，并引入动态稀疏注意力机制，结合自适应阈值策略剔除虚假连接，兼顾准确性与可解释性。统计随机化测试进一步过滤假阳性，提升因果推断可靠性。在金融与营销数据集上的实验表明，该模型优于 TCDF、GCFormer、CausalFormer 等现有方法，在噪声环境下仍能精确估计因果延迟，大幅减少误发现。注意力热力图揭示了广告对消费行为的中介效应及宏观经济指标对金融市场的影响等隐含模式。案例研究显示其能有效识别潜在中介变量与滞后因果因子，适用于高维动态场景。结合 RMSNorm 稳定性与因果掩码设计，模型具备良好的可扩展性与跨领域适应能力。
原文摘要 · Abstract (English)
Understanding causal relationships in multivariate time series (MTS) is essential for effective decision-making in fields such as finance and marketing, where complex dependencies and lagged effects challenge conventional analytical approaches. We introduce Dynamic Sparse Causal-Attention Temporal Networks for Interpretable Causality Discovery in MTS (DyCAST-Net), a novel architecture designed to enhance causal discovery by integrating dilated temporal convolutions and dynamic sparse attention mechanisms. DyCAST-Net effectively captures multiscale temporal dependencies through dilated convolutions while leveraging an adaptive thresholding strategy in its attention mechanism to eliminate spurious connections, ensuring both accuracy and interpretability. A statistical shuffle test validation further strengthens robustness by filtering false positives and improving causal inference reliability. Extensive evaluations on financial and marketing datasets demonstrate that DyCAST-Net consistently outperforms existing models such as TCDF, GCFormer, and CausalFormer. The model provides a more precise estimation of causal delays and significantly reduces false discoveries, particularly in noisy environments. Moreover, attention heatmaps offer interpretable insights, uncovering hidden causal patterns such as the mediated effects of advertising on consumer behavior and the influence of macroeconomic indicators on financial markets. Case studies illustrate DyCAST-Net's ability to detect latent mediators and lagged causal factors, making it particularly effective in high-dimensional, dynamic settings. The model's architecture enhanced by RMSNorm stabilization and causal masking ensures scalability and adaptability across diverse application domains
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