A variety of causal inference methods has been introduced to neuroimaging in recent years, including Causal Bayesian Networks, Dynamic Causal Modeling (DCM), Granger Causality, and Linear Non-Gaussian Acyclic Models (LINGAM). While all these methods aim to provide insights into how brain processes interact, they are based on rather different concepts of causality. In this talk, I will review the theoretical foundations of each of these methods, describe their inherent assumptions, and discuss the resulting consequences for the analysis and interpretation of neuroimaging data.