In recent years, the growing interest in methods of causal structure learning (CSL) has been confronted with a lack of access to a well-defined ground truth within real-world scenarios to evaluate these methods. Existing synthetic benchmarks are limited in their scope. They are either restricted to a “static” low-dimensional data set or do not allow examining mixed discrete-continuous or nonlinear data. This work introduces the mixed additive noise model that provides a ground truth framework for generating observational data following various distribution models. Moreover, we present our reference implementation MANM-CS that provides easy access and demonstrate how our framework can support researchers and practitioners. Further, we propose future research directions and possible extensions.