Inferring Causal Dependencies between Chaotic Dynamical Systems from Sporadic Time Series

Discovering causal structures of processes is a major tool of scientific inquiry because it helps us better understand and explain the mechanisms driving a phenomenon of interest, thereby facilitating analysis, reasoning, and synthesis for such systems. However, accurately inferring causal structures within a phenomenon 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).