Causal discovery in time series is a rapidly evolving field with a wide
variety of applications in other areas such as climate science and
neuroscience. Traditional approaches assume a stationary causal graph, which
can be adapted to nonstationary time series with time-dependent effects or
heterogeneous noise. In this work we address nonstationarity via
regime-dependent causal structures. We first establish identifiability for
high-order Markov Switching Models, which provide the foundations for
identifiable regime-dependent causal discovery. Our empirical studies
demonstrate the scalability of our proposed approach for high-order
regime-dependent structure estimation, and we illustrate its applicability on
brain activity data.

通过建立高阶马尔可夫切换模型的可辨识性，我们提出了基于制度依赖因果关系的发现方法，并通过实证研究展示了该方法在高阶制度依赖结构估计上的可扩展性，并对脑活动数据的适用性进行了说明。

使用高阶 Markov 切换模型识别非平稳因果结构

Identifying Nonstationary Causal Structures with High-Order Markov  Switching Models

Identifiability of latent variable models has recently gained interest in
terms of its applications to interpretability or out of distribution
generalisation. In this work, we study identifiability of Markov Switching
Models as a first step towards extending recent results to sequential latent
variable models. We present identifiability conditions within first-order
Markov dependency structures, and parametrise the transition distribution via
non-linear Gaussians. Our experiments showcase the applicability of our
approach for regime-dependent causal discovery and high-dimensional time series
segmentation.

本文研究了 Markov Switching Models 的可识别性，探讨了其在序列潜变量模型中的应用，并给出了基于非线性高斯的转移分布参数化方法，实验结果表明此方法可用于因果发现和高维时间序列分割。