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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