Causal Discovery over Clusters of Variables in Non-Markovian Systems

Causal discovery is the task of leveraging observational data to uncover causal relationships between variables. Recent work has extended these methods to operate over clusters of variables to improve scalability in high-dimensions and enable reasoning over higher-level entities. These approaches have been limited by strong assumptions including causal sufficiency. In this work, we introduce an approach for causal discovery over clusters in non-Markovian systems. First, we extend theory of graphical models in a knowledge-based context, to motivate introduction of a novel graphical equivalence class that can accommodate unobserved confounding. Then, we present a sound algorithm for causal discovery of learnable relationships between clusters of variables.