  Many researchers have voiced their support towards Pearl's counterfactual
theory of causation as a stepping stone for AI/ML research's ultimate goal of
intelligent systems. As in any other growing subfield, patience seems to be a
virtue since significant progress on integrating notions from both fields takes
time, yet, major challenges such as the lack of ground truth benchmarks or a
unified perspective on classical problems such as computer vision seem to
hinder the momentum of the research movement. This present work exemplifies how
the Pearl Causal Hierarchy (PCH) can be understood on image data by providing
insights on several intricacies but also challenges that naturally arise when
applying key concepts from Pearlian causality to the study of image data.
