Shadow-Mapping for Unsupervised Neural Causal Discovery
- URL: http://arxiv.org/abs/2104.08183v1
- Date: Fri, 16 Apr 2021 15:50:03 GMT
- Title: Shadow-Mapping for Unsupervised Neural Causal Discovery
- Authors: Matthew J. Vowels, Necati Cihan Camgoz and Richard Bowden
- Abstract summary: We describe 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.
- Score: 37.03455364275332
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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