How a built model is queried.

Bayesian Risk Decisions → A Bayesian risk model has a canonical architecture.

Every domain is different. But the structure of a Bayesian risk model is not. Five variable types appear in every well-formed risk model, in a specific causal order, with specific relationships between them. Recognizing this architecture is what lets a domain expert build a model systematically rather than staring at a blank graph — and what lets the same model answer three operationally distinct questions without rebuilding anything.

Influence Diagrams → A Bayesian network models uncertainty. An influence diagram models decisions.

Adding decision nodes and utility nodes to a Bayesian network converts a risk model into a decision model. The graph no longer just describes what might happen — it computes what you should do.

Value of Information → Should you act now, or gather more evidence first?

Value of Information is the formal answer to the most common executive decision: is it worth investigating further before committing? A causal model computes it precisely — the expected improvement in the decision if a specific uncertainty were resolved, compared to the cost of resolving it.

Diagnostic Reasoning → The model that predicts the failure is the same model that diagnoses it.

Bayes' theorem is symmetric. Set evidence on a cause, and the network predicts the effect. Set evidence on the effect, and the same network infers the most probable cause. The graph does not change. The parameters do not change. Only the direction of the query changes — and the diagnostic direction is what every post-mortem, every anomaly investigation, and every root-cause analysis requires.

Sensitivity Analysis → What if the no-confounding assumption is wrong?

Almost every observational causal claim rests on a load-bearing assumption: no unmeasured confounding. The conclusion is right if there’s no hidden variable doing the work we’re attributing to the treatment. The assumption cannot be tested directly — if it could, you’d measure the variable.