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：遵循受限结构因果模型框架对观测数据进行因果推断，主要依赖于数据生成机制（如非高斯性或非线性）中因果与效果之间的不对称性。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。本研究提出了一类新的受限结构因果模型，该模型通过时变滤波器和平稳噪声实现，并利用非平稳性中的不对称性进行双变量及网络设置下的因果识别。我们通过利用缓慢变化过程双变量演化谱的有效估计，提出了高效的程序。通过评估涉及高阶与非平滑滤波器的多种合成与真实数据集，验证了所提方法的有效性。