It is commonplace to encounter nonstationary data, of which the underlying
generating process may change over time or across domains. The nonstationarity
presents both challenges and opportunities for causal discovery. In this paper
we propose a principled framework to handle nonstationarity, and develop some
methods to address three important questions. First, we propose an enhanced
constraint-based method to detect variables whose local mechanisms are
nonstationary and recover the skeleton of the causal structure over observed
variables. Second, we present a way to determine some causal directions by
taking advantage of information carried by changing distributions. Third, we
develop a method for visualizing the nonstationarity of causal modules.
Experimental results on various synthetic and real-world data sets are
presented to demonstrate the efficacy of our methods.

本文提出了一个有原则的框架来处理非平稳数据，并开发了一些方法来处理三个重要问题：检测局部机制不平稳的变量以及恢复观测变量之间的因果结构，通过利用变化分布所携带的信息确定某些因果方向，以及开发可视化因果模块的非平稳方法。作者通过各种合成和真实世界数据集的实验结果来展示这些方法的有效性。