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✅ '''Internal validity''' measures the degree to which a study or analysis correctly establishes a causal relationship between a treatment and an outcome within the specific context examined, free from systematic errors such as [[Definition:Selection bias | selection bias]], [[Definition:Confounding variable | confounding]], or measurement error. In insurance, internal validity is the benchmark against which [[Definition:Actuary | actuaries]], [[Definition:Data scientist | data scientists]], and [[Definition:Underwriting | underwriters]] evaluate whether observed relationships in their data — between a [[Definition:Fraud detection | fraud detection]] model and recovery rates, between a [[Definition:Wellness program | wellness intervention]] and [[Definition:Claim | claims]] frequency, or between a [[Definition:Pricing | pricing]] change and [[Definition:Lapse rate | policyholder retention]] — reflect genuine causal effects rather than artifacts of how the data was generated or analyzed.

🔍 Achieving high internal validity in insurance research is challenging because true randomized experiments are rarely feasible. Insurers cannot randomly assign policyholders to different coverage levels or withhold safety interventions for the sake of a clean control group. Instead, analysts rely on quasi-experimental methods — [[Definition:Instrumental variable (IV) | instrumental variables]], [[Definition:Interrupted time series analysis (ITSA) | interrupted time series]], regression discontinuity designs, [[Definition:Propensity score matching | propensity score matching]], and [[Definition:Heckman selection model | Heckman corrections]] — each of which addresses specific threats to internal validity. The choice of method depends on the nature of the threat: if the primary concern is self-selection into a [[Definition:Telematics | telematics]] program, matching or IV methods may be appropriate; if the question involves the impact of a regulatory change at a known date, an interrupted time series design may offer the strongest identification. Sensitivity analyses that probe the [[Definition:Ignorability assumption | ignorability assumption]] and assess robustness to unmeasured confounders are standard practice for demonstrating that findings withstand scrutiny.

🏛️ Strong internal validity is not merely an academic aspiration — it has direct commercial and regulatory implications. [[Definition:Regulator | Regulators]] across major markets, including those operating under [[Definition:Solvency II | Solvency II]], the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] framework, and [[Definition:C-ROSS | C-ROSS]], expect insurers to substantiate the assumptions embedded in their [[Definition:Predictive model | predictive models]] and [[Definition:Reserving | reserving]] methodologies. A pricing model built on internally invalid analyses — where, for example, [[Definition:Healthy user bias | healthy user bias]] or [[Definition:Immortal time bias | immortal time bias]] inflated the estimated benefit of a risk factor — can lead to systematic [[Definition:Underpricing | underpricing]], [[Definition:Reserve | reserve]] deficiencies, and regulatory challenge. For [[Definition:Reinsurer | reinsurers]] and [[Definition:Insurance-linked security (ILS) | ILS]] investors evaluating cedant performance, the internal validity of the underlying analytics is a proxy for the reliability of projected outcomes, making it a quietly decisive factor in capital allocation decisions.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Causal inference]]
* [[Definition:Selection bias]]
* [[Definition:Ignorability assumption]]
* [[Definition:External validity]]
* [[Definition:Confounding variable]]
* [[Definition:Predictive model]]
{{Div col end}}

Definition:Internal validity - Revision history

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