Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity. 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:根据有限的结构性因果模型框架(SCM)的观察数据得出的因果推断主要取决于数据生成机制(如非加盟或非线性)的原因和影响之间的不对称性,这一方法可以适应固定的时间序列,但从非静止时间序列推断因果关系仍是一项艰巨的任务。在这项工作中,我们建议通过时间变化的过滤器和固定的噪音,采用新的限制的SCM类别,利用非静止的不对称性,在双轨和网络环境中进行因果关系识别。我们提出有效的程序,利用双轨进化光谱的强力估计来缓慢变化过程。对涉及高顺序和非移动过滤器的各种合成和真实数据集进行了评估,以证明我们拟议方法的有效性。