An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit `mirage' correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.
翻译:多数科学领域的一个重要目标是通过一系列观测发现因果结构。 不幸的是,基于相互关系或相互信息的因果发现方法往往无法确定显示动态关系的系统中的因果联系。这些动态系统(包括著名的联合后勤地图)显示“幻影”相关关系,这些关联因观察窗口而出现和消失。这意味着不仅相关关系不是因果,而且可能是反直觉的,因果关系可能发生时没有关联。在本文件中,我们描述了神经暗影图,这是一种基于神经网络的基于神经网络的方法,将高维视频数据嵌入一个低维的暗影图中,以便随后估计因果联系。我们展示其从动态系统的视频代表中发现因果联系的性能。