  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.
