Structural causal models (SCMs) constitute a rigorous and tested formalism to deal with causality in many fields, including artificial intelligence and machine learning. Systems and phenomena may be modelled as SCMs and then studied using the tools provided by the framework of causality. A given system can, however, be modelled at different levels of abstraction, depending on the aims or the resources of a modeller. The most exemplar case is probably statistical physics, where a thermodynamical system may be represented both as a collection of microscopic particles or as a single body with macroscopic properties. In general, however, switching between models with different granularities presents non-trivial challenges and raises questions of consistency. These slides will first provide a brief introduction to SCMs, and then consider how we can express the problem of relating SCMs representing the same phenomenon at different levels of abstraction. Finally, we will discuss open challenges and present some existing solutions, as well as pointing towards possible future directions of research.