### Causal discovery with unobserved confounding and non- Gaussian data

*Submitted, 2020+ *

*Wang, Y. S., Drton, M.*

We consider data which arise from a linear structural equation model in which the idiosyncratic errors are allowed to be dependent in order to capture possible latent confounding. We show that under certain restrictions on the latent confounding and when the errors are non-Gaussian, the exact causal structure–not merely an equivalence class–can be consistently recovered from purely observational data when the graph corresponding to the SEM is bow-free and acyclic.