Inferring Causal Dependencies Between Chaotic Dynamical Systems from Sporadic Time Series
Abstract
Discovering causal structures of processes is a major tool of scientific inquiry because it helps better understand and explain the mechanisms driving a phenomenon of interest. However, accurately inferring causal structures based on observational data only is still an open problem. In particular, this problem becomes increasingly difficult when it relies on data with missing values. In this article, we present a method to uncover causal relations between chaotic dynamical systems from sporadic time series (that is, incomplete observations at infrequent and irregular intervals), which builds upon Convergent Cross Mapping and recent advances in continuous time-series modeling (GRU-ODE-Bayes).
Cite
Text
De Brouwer et al. "Inferring Causal Dependencies Between Chaotic Dynamical Systems from Sporadic Time Series." ICML 2020 Workshops: Artemiss, 2020.Markdown
[De Brouwer et al. "Inferring Causal Dependencies Between Chaotic Dynamical Systems from Sporadic Time Series." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/brouwer2020icmlw-inferring/)BibTeX
@inproceedings{brouwer2020icmlw-inferring,
title = {{Inferring Causal Dependencies Between Chaotic Dynamical Systems from Sporadic Time Series}},
author = {De Brouwer, Edward and Arany, Adam and Simm, Jaak and Moreau, Yves},
booktitle = {ICML 2020 Workshops: Artemiss},
year = {2020},
url = {https://mlanthology.org/icmlw/2020/brouwer2020icmlw-inferring/}
}