TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
TTCD：融合 Transformer 的非平稳时间序列因果发现AI 解读 ↓
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TTCD：融合 Transformer 的非平稳时间序列因果发现
- 研究问题
- 在非平稳、非线性和含噪时间序列中，如何同时发现同期与滞后因果关系，并减少有限样本下的伪相关干扰？
- 方法与思路
- 作者结合时域与频域注意力、动态非平稳特征学习和 Transformer 解码重构，提炼信号后交由因果结构学习器推断图。
- 材料陈述的贡献
- 摘要称，TTCD 在合成、基准和真实数据上优于比较方法，并能提高结构准确性及与领域知识的一致性。
- 阅读关注点
- 应核对因果方向的识别依据、潜在混杂处理、重构与结构学习的连接机制，以及基准比较是否使用一致的信息和调参预算。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明完整识别条件，不能将预测依赖直接视为已识别因果。
原文摘要
The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust causal discovery methods that can identify intricate contemporaneous and lagged relationships in non-stationary, nonlinear, and noisy settings. Existing constraint-based methods often rely heavily on conditional independence tests that degrade for limited data samples and complex distributions, while score-based methods impose strong statistical assumptions. Recent methods address special cases such as change point detection or distribution shifts, but struggle to provide a unified solution. We propose the Transformer Integrated Temporal Causal Discovery (TTCD) Framework, a novel end-to-end approach that learns contemporaneous and lagged causal relations from non-stationary time series. TTCD introduces a Non-Stationary Feature Learner integrating temporal and frequency-domain attention with dynamic non-stationarity profiling, and a custom Causal Structure Learner. A key innovation is reconstruction-guided causal signal distillation, to distill essential causal signals through the reconstruction process of the transformer decoder, which mitigates noise and spurious correlations while preserving meaningful dependencies. The Causal Structure Learner operates on distilled reconstructed signals to infer the underlying causal graph without restrictive assumptions on noise distributions or data generation processes. Experiments on synthetic, benchmark, and real world datasets show that TTCD consistently outperforms state-of-the-art baselines in both accuracy and consistency with domain knowledge, demonstrating the approach's effectiveness for causal discovery in challenging real world contexts.