Time series causal discovery is essential for understanding dynamic systems, yet many existing methods remain sensitive to noise, non-stationarity, and sampling variability. We propose the Validated Consensus-Driven Framework (VCDF), a simple and method-agnostic layer that improves robustness by evaluating the stability of causal relations across blocked temporal subsets. VCDF requires no modification to base algorithms and can be applied to methods such as VAR-LiNGAM and PCMCI. Experiments on synthetic datasets show that VCDF improves VAR-LiNGAM by approximately 0.08-0.12 in both window and summary F1 scores across diverse data characteristics, with gains most pronounced for moderate-to-long sequences. The framework also benefits from longer sequences, yielding up to 0.18 absolute improvement on time series of length 1000 and above. Evaluations on simulated fMRI data and IT-monitoring scenarios further demonstrate enhanced stability and structural accuracy under realistic noise conditions. VCDF provides an effective reliability layer for time series causal discovery without altering underlying modeling assumptions.

翻译：时间序列因果发现对于理解动态系统至关重要，然而许多现有方法仍对噪声、非平稳性和采样变异性敏感。我们提出了经过验证的共识驱动框架（VCDF），这是一种简单且与具体方法无关的增强层，通过评估因果关系在分块时间子集上的稳定性来提高鲁棒性。VCDF无需修改基础算法，可应用于VAR-LiNGAM和PCMCI等方法。在合成数据集上的实验表明，VCDF将VAR-LiNGAM的窗口F1分数和摘要F1分数在不同数据特征下均提升了约0.08-0.12，这种提升在中长序列中最为显著。该框架还能从更长序列中获益，在长度1000及以上的时间序列上实现了最高0.18的绝对提升。在模拟fMRI数据和IT监控场景下的评估进一步表明，VCDF在真实噪声条件下具有更强的稳定性和结构准确性。VCDF为时间序列因果发现提供了一个有效的可靠性增强层，且不改变底层建模假设。