Learning the structure of causal directed acyclic graphs (DAGs) is useful in
many areas of machine learning and artificial intelligence, with wide
applications. However, in the high-dimensional setting, it is challenging to
obtain good empirical and theoretical results without strong and often
restrictive assumptions. Additionally, it is questionable whether all of the
variables purported to be included in the network are observable. It is of
interest then to restrict consideration to a subset of the variables for
relevant and reliable inferences. In fact, researchers in various disciplines
can usually select a set of target nodes in the network for causal discovery.
This paper develops a new constraint-based method for estimating the local
structure around multiple user-specified target nodes, enabling coordination in
structure learning between neighborhoods. Our method facilitates causal
discovery without learning the entire DAG structure. We establish consistency
results for our algorithm with respect to the local neighborhood structure of
the target nodes in the true graph. Experimental results on synthetic and
real-world data show that our algorithm is more accurate in learning the
neighborhood structures with much less computational cost than standard methods
that estimate the entire DAG. An R package implementing our methods may be
accessed at this https URL

通过开发一种新的基于约束的方法，该方法用于估计多个用户指定目标节点周围的局部结构，从而在邻域之间实现结构学习协调，进而促进无需学习整个有向无环图结构的因果发现。实验结果表明，我们的算法在学习邻域结构时具有更高的准确性，且计算成本较低于传统方法。

在有向无环图上进行多邻域协同学习

Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graph

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。