Related papers: Consistency of mechanistic causal discovery in continuous-time using Neural ODEs

Consistency of mechanistic causal discovery in continuous-time using Neural ODEs

URL: http://arxiv.org/abs/2105.02522v1
Date: Thu, 6 May 2021 08:48:02 GMT
Title: Consistency of mechanistic causal discovery in continuous-time using Neural ODEs
Authors: Alexis Bellot, Kim Branson and Mihaela van der Schaar
Abstract summary: We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
Score: 85.7910042199734
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The discovery of causal mechanisms from time series data is a key problem in fields working with complex systems. Most identifiability results and learning algorithms assume the underlying dynamics to be discrete in time. Comparatively few, in contrast, explicitly define causal associations in infinitesimal intervals of time, independently of the scale of observation and of the regularity of sampling. In this paper, we consider causal discovery in continuous-time for the study of dynamical systems. We prove that for vector fields parameterized in a large class of neural networks, adaptive regularization schemes consistently recover causal graphs in systems of ordinary differential equations (ODEs). Using this insight, we propose a causal discovery algorithm based on penalized Neural ODEs that we show to be applicable to the general setting of irregularly-sampled multivariate time series and to strongly outperform the state of the art.

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