magnus919_agent-skills/actuarial-risk-modeling/references/problem-framing.md

Problem Framing

Start with the decision

A model is fit for purpose only relative to a decision. Record who acts, what action is available, when the action occurs, what information exists then, and what error costs matter. A request such as “predict risk” is incomplete until risk is defined as an outcome, probability, loss, quantile, aggregate, or time-to-event quantity.

Classify the question

Question	Target	Main danger
Descriptive	What patterns are present?	Treating association as effect
Predictive	What will be observed later?	Leakage and population shift
Causal	What would change under intervention?	Confounding and unsupported counterfactuals
Decision	Which action has better expected consequence?	Optimizing a proxy that is not the decision

Use causal language only when the design and identification strategy support it. A predictive variable can be useful without being a cause, and a causal variable can be poorly predictive.

Define the observational unit

Write the row grain explicitly: policy-period, claim, payment development cell, customer-month, firm-quarter, or event episode. Check whether rows are independent. If an entity appears multiple times, decide whether the task needs clustered errors, fixed/random effects, recurrent-event methods, a hierarchical model, or aggregation.

Define exposure and windows

Counts and rates need a denominator or offset that represents opportunity. Define policy in-force time, earned exposure, person-time, account months, or trading time. Define the origin, observation window, development window, and prediction horizon separately. Do not label an event “absent” when it could occur after the observation window.

Data contract questions

• What is the source, extraction time, version, and authoritative field?
• Which fields were known at scoring time, and which were revised afterward?
• Are claims incurred, reported, paid, or developed? Are losses nominal or real?
• Are zeros structural, censored, missing, or a real measured value?
• Are large observations plausible, data errors, or a distinct regime?
• What populations are excluded, and could exclusion depend on the outcome?
• Which variables are legally, ethically, operationally, or contractually permitted?
• Which missingness, exposure, and grouping decisions must be preserved in provenance?

Estimands and outputs

State whether the output is an expected value, event probability, rate, quantile, tail mean, reserve, volatility, survival probability, ranking, or scenario distribution. Name the conditioning population and horizon. For a two-part loss process, distinguish P(Y > 0 | X) from E[Y | Y > 0, X] and explain how they are combined.

Escalation

Stop and ask for clarification when the outcome grain, exposure, decision boundary, or information availability cannot be recovered. Do not fill those gaps with a plausible industry convention. For regulated or consequential use, route the completed brief to a qualified practitioner and the applicable standard or policy owner.