A neural network can be accurate and still leave us without a usable account of why a decision obtained. Causal language promises more than correlation heatmaps: interventions, counterfactuals, and graphs that make assumptions explicit.
This essay is forthcoming. The working claim is simple: causal structure is valuable when it constrains what we are allowed to say - not when it merely decorates a prediction.