How to read these
Each case opens with what the standard tooling was saying, names the question the decision actually required, and walks through what a causal model showed instead. Model files ship with every case.
The cases
Property Insurance → The intervention that makes things worse.
A board considering a 15% rate increase on a $412M coastal portfolio. The causal model showed the increase would accelerate adverse selection — and identified the cheaper fix the register couldn’t see.
Insurance Reserving → Chain-ladder tells you the pattern. It cannot tell you why the pattern is changing.
Social inflation, attorney involvement, severity creep, or a mix — each carries different reserve implications. Regulators are increasingly asking for the why; the standard tooling cannot supply it.
Construction walkthrough → how the model was built across eight weeks — expert sessions, disagreements, validation. For the technical evaluator.
Insurance Attribution → The expert who was right for the wrong reason.
The senior adjuster apportions every claim correctly — drawing on jurisdiction, injury pattern, and two decades of intuition. When she retires, the reasoning goes with her. The correlate may not hold in the next case.
Self-Insurance Decision → Most self-insurance analysis treats future losses as a fixed distribution.
The act of self-insuring changes the distribution — through claims behavior, loss-prevention investment, and risk culture. A causal model represents the change; an actuarial mean does not.
Next
For the methods behind these cases, see the Querying cluster (how a built model is interrogated for decisions) and the Causal Modeling foundation. For the wider portfolio across all eight domains, see Cases.