The ultimate aim of many data analyses is to infer cause-and-effect relationships between random variables of interest. While much of the available methodology for addressing causal questions relies on structural causal models, these models are best suited for systems without feedback loops. Extensions to accommodate feedback have been proposed, but often result in models that are challenging to interpret. In this lecture, we present an alternative approach to graphical causal modeling that considers stationary distributions of multivariate diffusion processes.