Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this work, we propose a new class of restricted SCM, via a time-varying filter and stationary noise, and exploit the asymmetry from nonstationarity for causal identification in both bivariate and network settings. We propose efficient procedures by leveraging powerful estimates of the bivariate evolutionary spectra for slowly varying processes. Various synthetic and real datasets that involve high-order and non-smooth filters are evaluated to demonstrate the effectiveness of our proposed methodology.

翻译：在受限结构因果模型（SCM）框架下，从观测数据中进行因果推断主要依赖于数据生成机制中因果之间的不对称性，例如非高斯性或非线性。该方法可适用于平稳时间序列，但从非平稳时间序列中推断因果关系仍是一项具有挑战性的任务。本文通过时变滤波器和平稳噪声提出了一类新的受限SCM，并利用非平稳性带来的不对称性在双变量及网络场景下实现因果识别。我们通过利用对缓慢变化过程的双变量演化谱的强大估计，提出了高效的推断流程。通过对涉及高阶与非平滑滤波器的多种合成与真实数据集进行评估，验证了所提方法的有效性。