  Causal discovery from observational data is a challenging task that can only
be solved up to a set of equivalent solutions, called an equivalence class.
Such classes, which are often large in size, encode uncertainties about the
orientation of some edges in the causal graph. In this work, we propose a new
set of assumptions that constrain possible causal relationships based on the
nature of variables, thus circumscribing the equivalence class. Namely, we
introduce typed directed acyclic graphs, in which variable types are used to
determine the validity of causal relationships. We demonstrate, both
theoretically and empirically, that the proposed assumptions can result in
significant gains in the identification of the causal graph. We also propose
causal discovery algorithms that make use of these assumptions and demonstrate
their benefits on simulated and pseudo-real data.
