论文标题
因果推断，从缓慢变化的非组织过程
Causal Inference from Slowly Varying Nonstationary Processes
论文作者
论文摘要
遵循限制的结构因果模型（SCM）框架的观察数据的因果推断很大程度上取决于数据生成机制（例如非高斯性或非线性）的因素和影响之间的不对称性。该方法可以适应固定时间序列，但是从非组织时间序列推断出的因果关系仍然是一项艰巨的任务。在这项工作中，我们通过随时间变化的过滤器和固定噪声提出了一类新的限制SCM，并利用非平稳性的不对称性在双变量和网络设置中用于因果鉴定。我们通过利用对慢速变化的过程的双变量进化光谱的强大估计来提出有效的程序。评估涉及高阶和非平滑滤波器的各种合成和真实数据集，以证明我们提出的方法的有效性。
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.
