论文标题
从观察时间序列推断出扩展的摘要因果图
Inferring extended summary causal graphs from observational time series
论文作者
论文摘要
这项研究解决了学习扩展的摘要因果图的问题。我们提出的算法适合因果发现基于著名的约束框架，并利用信息理论措施来确定时间序列之间的依赖关系（在）依赖性。我们首先将因果熵度量的概括性介绍给任何滞后或瞬时关系，然后使用此措施通过调整两个众所周知的算法，即PC和FCI来构建扩展的摘要图形。通过在模拟和真实数据集上运行的几个实验来说明我们方法的行为。
This study addresses the problem of learning an extended summary causal graph on time series. The algorithms we propose fit within the well-known constraint-based framework for causal discovery and make use of information-theoretic measures to determine (in)dependencies between time series. We first introduce generalizations of the causation entropy measure to any lagged or instantaneous relations, prior to using this measure to construct extended summary causal graphs by adapting two well-known algorithms, namely PC and FCI. The behavior of our methods is illustrated through several experiments run on simulated and real datasets.
