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🔎 '''Partial identification''' is a statistical framework in which analysts derive a bounded range of plausible values for a parameter of interest rather than a single point estimate, acknowledging that the available data and assumptions are insufficient to pin down an exact answer. In insurance and [[Definition:Actuarial science | actuarial]] contexts, partial identification arises naturally: when studying the causal effect of a [[Definition:Risk mitigation | risk mitigation]] program on [[Definition:Claims frequency | claims frequency]], for example, the data often cannot rule out multiple explanations, and honest analysis may only narrow the true effect to an interval rather than a precise number.

⚙️ The framework, pioneered by economist Charles Manski and extended by subsequent researchers, replaces the traditional approach of imposing strong (and often untestable) assumptions to achieve point identification with a strategy of stating only the assumptions one is willing to defend and reporting the set of estimates consistent with them. In practice, an insurer evaluating whether a [[Definition:Telematics | telematics]]-based [[Definition:Discount | discount]] program genuinely reduces [[Definition:Loss ratio (L/R) | loss ratios]] — rather than simply attracting safer drivers — may find that without a [[Definition:Randomized controlled trial | randomized experiment]], the true effect lies somewhere within an identifiable bound. Partial identification methods use [[Definition:Observational data | observational data]] combined with inequality constraints, monotonicity conditions, or shape restrictions to tighten these bounds as far as the evidence permits. The output is an interval — say, a 2 to 8 percentage-point reduction in loss ratio — that honestly communicates the remaining uncertainty. This is especially relevant when data is censored (as with policies that have not yet reached full [[Definition:Loss development | development]]) or when unobserved [[Definition:Confounding variable | confounders]] cannot be measured directly.

💡 For decision-makers across insurance, the value of partial identification lies in its intellectual honesty: it prevents overconfident conclusions that can lead to [[Definition:Mispricing | mispricing]], misallocated [[Definition:Capital | capital]], or flawed [[Definition:Reserve | reserve]] estimates. Regulators in sophisticated supervisory regimes — particularly those emphasizing [[Definition:Own Risk and Solvency Assessment (ORSA) | ORSA]] processes under [[Definition:Solvency II | Solvency II]] or [[Definition:Enterprise risk management (ERM) | enterprise risk management]] standards — increasingly expect insurers to articulate the range of uncertainty around key assumptions rather than present a single deterministic scenario. As the industry integrates more complex causal inference techniques into [[Definition:Predictive modeling | predictive modeling]] and pricing, partial identification offers a principled middle ground between making strong parametric assumptions and abandoning causal analysis altogether.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Observational data]]
* [[Definition:Randomized controlled trial]]
* [[Definition:Propensity score matching]]
* [[Definition:Proxy variable]]
* [[Definition:Actuarial model]]
* [[Definition:Regression discontinuity]]
{{Div col end}}

Definition:Partial identification - Revision history

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