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📊 '''G-computation''' is a parametric [[Definition:Causal inference | causal inference]] method — originally developed within epidemiology by James Robins — that estimates the causal effect of a treatment or exposure by modeling the outcome as a function of the treatment and confounders, then standardizing predictions across the entire population under each treatment scenario. In the insurance industry, G-computation provides [[Definition:Actuarial science | actuaries]] and [[Definition:Data scientist | data scientists]] with a principled framework for answering counterfactual questions: what would [[Definition:Loss experience | loss experience]] look like if every policyholder in a portfolio had been subject to a particular [[Definition:Underwriting | underwriting]] action, compared with the scenario where none had?

⚙️ The procedure begins by fitting an outcome model — typically a [[Definition:Generalized linear model (GLM) | GLM]] or another suitable regression — relating the outcome variable (such as [[Definition:Claims frequency | claims frequency]] or [[Definition:Loss severity | claim severity]]) to the treatment indicator and a set of measured [[Definition:Confounding variable | confounders]]. Rather than simply reading off a coefficient, the analyst uses the fitted model to predict outcomes for every individual in the dataset under both the treatment and control conditions, regardless of their actual treatment status. The average difference between these two sets of predictions yields the estimated causal effect. A [[Definition:Health insurance | health insurer]] might use G-computation to estimate the population-level impact of a chronic-disease management program by predicting each member's expected medical costs with and without enrollment, adjusting for age, comorbidities, and plan type. Because the method relies on a fully specified outcome model, its validity hinges on correct model specification and the assumption that all relevant confounders have been measured — the standard [[Definition:Backdoor criterion | no-unmeasured-confounding]] requirement.

🛡️ One of G-computation's practical advantages for insurance applications is that it yields population-level causal estimates that can be directly translated into financial projections — a natural fit for an industry that thinks in terms of portfolio-wide [[Definition:Premium | premium]] adequacy, [[Definition:Reserve | reserve]] sufficiency, and [[Definition:Loss ratio (L/R) | loss ratio]] impact. Unlike methods that produce local effects for narrow subpopulations, G-computation produces an average treatment effect across the full book, which aligns with how [[Definition:Chief actuary | chief actuaries]] and [[Definition:Chief underwriting officer (CUO) | underwriting leaders]] evaluate strategic decisions. The method can also be extended to handle time-varying treatments and exposures — relevant, for instance, when assessing the cumulative effect of successive [[Definition:Risk mitigation | loss-control]] interventions over multiple [[Definition:Policy period | policy periods]]. As [[Definition:Insurtech | insurtech]] firms and traditional carriers alike invest in building causal modeling capabilities, G-computation occupies a central place in the toolkit alongside [[Definition:Inverse probability weighting (IPW) | inverse probability weighting]] and [[Definition:Doubly robust estimation | doubly robust estimators]], offering a transparent and interpretable approach to quantifying the real-world impact of insurance interventions.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Causal inference]]
* [[Definition:Inverse probability weighting (IPW)]]
* [[Definition:Doubly robust estimation]]
* [[Definition:Counterfactual]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Average treatment effect (ATE)]]
{{Div col end}}

Definition:G-computation - Revision history

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