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

Pearl Causal Hierarchy on Image Data: Intricacies & Challenges