A pervasive task found throughout the empirical sciences is to determine the effect of interventions from observational data. It is well-understood that assumptions are necessary to perform such causal inferences, an idea popularized through Cartwright’s motto: "no causes-in, no causes-out." One way of articulating these assumptions is through the use of causal diagrams, which are a special type of graphical model with causal semantics [Pearl, 2000]. The graphical approach has been applied successfully in many settings, but there are still challenges to its use, particularly in complex, high-dimensional domains. In this talk, I will introduce cluster causal diagrams (C-DAGs), a novel causal graphical model that allows for the partial specification of the relationships among variables. C-DAGs provide a simple yet effective way to partially abstract a grouping (cluster) of variables among which causal relationships are not fully understood while preserving consistency with the underlying causal system and the validity of causal identification tools. Reference: https://causalai.net/r77.pdf