How do you think about predictive power and performance for explanatory models?

Despite having been in the field for a minute, I find myself still a bit naïve on how to think about predictive power in explanatory focused models. I'm trying to build better intuition here and think the following scenario illustrates a bit of my confusion or ignorance. Let's assume in the below the business wants a dashboard with (binary) predictions on some set of events or entities but mostly cares about "why?". It's not a true pure causal inference problem, …

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