Once you unroll time, an HMM is just an ordinary Bayesian network with repeated structure. That makes temporal reasoning much less mysterious and much more programmable.

Counterfactuals are where causal APIs stop describing populations and start answering the harder question: what would have happened for this exact case?

If observing X and setting X collapse to the same operation in your tooling, confounding has already beaten you.

A treatment can look worse in the aggregate and better in every subgroup. That is not a corner case. It is the reason your query surface needs association, adjustment, and intervention.

Lead optimization does not stall because teams lack scores. It stalls because scores do not answer what analog to make next under competing objectives.