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📈 '''Regression adjustment''' is a statistical method widely used in insurance analytics to estimate the effect of a variable, intervention, or risk factor on an outcome while controlling for other observed covariates. Actuaries and data scientists across [[Definition:Life insurance | life]], [[Definition:Health insurance | health]], and [[Definition:Property and casualty insurance (P&C) | property and casualty]] lines rely on regression adjustment every day — whether they are isolating the impact of a [[Definition:Deductible | deductible]] change on [[Definition:Claim | claim]] frequency, estimating the incremental [[Definition:Loss ratio (L/R) | loss-ratio]] improvement attributable to a new [[Definition:Fraud detection | fraud model]], or controlling for demographic and geographic confounders when evaluating an [[Definition:Underwriting | underwriting]] variable's predictive power. At its core, the technique fits a mathematical model to observed data and uses the estimated coefficients to separate the contribution of each included factor.

⚙️ In practice, an analyst specifies a regression model — often a [[Definition:Generalized linear model (GLM) | generalized linear model]] given the non-normal distributions typical of insurance data — that includes both the variable of interest and a set of control variables believed to influence the outcome. For instance, when assessing whether a [[Definition:Telematics | telematics]] program genuinely reduces accident severity, the model would control for driver age, vehicle type, coverage limits, territory, and prior claims history. The coefficient on the telematics indicator then represents the program's estimated effect, net of those confounders. The approach is straightforward to implement and explain, but its validity hinges on correct model specification: if an important confounder is omitted or the functional form is wrong, the estimated treatment effect can be biased. This concern is particularly acute in insurance, where [[Definition:Adverse selection | adverse selection]] and [[Definition:Moral hazard | moral hazard]] introduce subtle, hard-to-observe confounders. Analysts often combine regression adjustment with other techniques — such as [[Definition:Propensity score matching (PSM) | propensity score matching]] or [[Definition:Instrumental variable | instrumental variables]] — to strengthen causal claims.

💡 Regulatory and commercial pressures make regression adjustment indispensable across global insurance markets. [[Definition:Insurance regulation | Regulators]] in Solvency II jurisdictions, the United States, and Asia-Pacific markets expect carriers to demonstrate that [[Definition:Rating factor | rating factors]] are actuarially justified and not proxies for prohibited characteristics — a task that fundamentally requires regression-based analysis to disentangle correlated risk drivers. Similarly, [[Definition:Reinsurance | reinsurers]] evaluating cedants' portfolios use regression techniques to benchmark [[Definition:Experience rating | experience]] and detect trends that simple aggregate statistics would obscure. For [[Definition:Insurtech | insurtech]] firms pitching data-enrichment products or alternative risk scores, regression adjustment is the workhorse method for demonstrating lift above incumbent models. While more sophisticated causal-inference tools have gained popularity, regression adjustment remains the starting point — and often the finishing point — for most analytical work in the industry.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Propensity score matching (PSM)]]
* [[Definition:Selection bias]]
* [[Definition:Quasi-experiment]]
* [[Definition:Rating factor]]
* [[Definition:Simpson's paradox]]
{{Div col end}}

Definition:Regression adjustment - Revision history

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