3 research outputs found
Discovering Nonlinear Relations with Minimum Predictive Information Regularization
Identifying the underlying directional relations from observational time
series with nonlinear interactions and complex relational structures is key to
a wide range of applications, yet remains a hard problem. In this work, we
introduce a novel minimum predictive information regularization method to infer
directional relations from time series, allowing deep learning models to
discover nonlinear relations. Our method substantially outperforms other
methods for learning nonlinear relations in synthetic datasets, and discovers
the directional relations in a video game environment and a heart-rate vs.
breath-rate dataset.Comment: 26 pages, 11 figures; ICML'19 Time Series Worksho
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Standard causal discovery methods must fit a new model whenever they
encounter samples from a new underlying causal graph. However, these samples
often share relevant information - for instance, the dynamics describing the
effects of causal relations - which is lost when following this approach. We
propose Amortized Causal Discovery, a novel framework that leverages such
shared dynamics to learn to infer causal relations from time-series data. This
enables us to train a single, amortized model that infers causal relations
across samples with different underlying causal graphs, and thus makes use of
the information that is shared. We demonstrate experimentally that this
approach, implemented as a variational model, leads to significant improvements
in causal discovery performance, and show how it can be extended to perform
well under hidden confounding
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Exploratory analysis of time series data can yield a better understanding of
complex dynamical systems. Granger causality is a practical framework for
analysing interactions in sequential data, applied in a wide range of domains.
In this paper, we propose a novel framework for inferring multivariate Granger
causality under nonlinear dynamics based on an extension of self-explaining
neural networks. This framework is more interpretable than other
neural-network-based techniques for inferring Granger causality, since in
addition to relational inference, it also allows detecting signs of
Granger-causal effects and inspecting their variability over time. In
comprehensive experiments on simulated data, we show that our framework
performs on par with several powerful baseline methods at inferring Granger
causality and that it achieves better performance at inferring interaction
signs. The results suggest that our framework is a viable and more
interpretable alternative to sparse-input neural networks for inferring Granger
causality.Comment: ICLR 202