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📋 '''Doubly robust estimation''' is a statistical technique that combines two modeling strategies — typically an outcome regression model and a propensity-score (treatment) model — to produce a causal estimate that remains consistent if at least one of the two models is correctly specified. For insurance analysts working with observational [[Definition:Claims | claims]] and policy data, this property is enormously valuable: it provides a built-in safety net against the model misspecification that plagues any single-model approach, making causal conclusions about [[Definition:Loss ratio | loss ratios]], [[Definition:Claims frequency | claims frequency]], or intervention effectiveness substantially more robust.

⚙️ In a typical insurance application, an analyst first builds a propensity-score model predicting, say, which [[Definition:Policyholder | policyholders]] opted into a [[Definition:Telematics | telematics]] program based on observable characteristics such as age, [[Definition:Loss history | loss history]], vehicle type, and [[Definition:Coverage | coverage]] tier. Simultaneously, the analyst specifies an outcome model relating those same covariates to the claims outcome of interest. The doubly robust estimator — often implemented through augmented inverse probability weighting (AIPW) — uses the propensity scores to reweight the data and the outcome model to adjust for any residual imbalance. If the propensity model captures the selection process well but the outcome model is slightly misspecified, the estimator still converges to the true causal effect, and vice versa. This dual protection is especially important in insurance, where the data-generating process involves complex interactions among risk characteristics, [[Definition:Adverse selection | adverse selection]] dynamics, and [[Definition:Moral hazard | moral hazard]] that no single parametric model is likely to capture perfectly.

💡 Beyond its statistical properties, doubly robust estimation appeals to insurance organizations because it aligns with a broader culture of conservatism and redundancy — the same instinct that drives insurers to hold [[Definition:Reserving | reserves]] above best estimates and to purchase [[Definition:Reinsurance | reinsurance]] against tail events. When an [[Definition:Actuarial science | actuarial]] or data science team presents a causal finding to leadership, regulators, or [[Definition:Reinsurance | reinsurance]] partners, the ability to say "our estimate is protected against misspecification in either of its component models" materially strengthens credibility. The technique is increasingly used in applications ranging from evaluating the effect of [[Definition:Loss control | loss-control]] programs on [[Definition:Workers' compensation insurance | workers' compensation]] severity, to assessing the impact of regulatory reforms across [[Definition:Solvency II | Solvency II]] and non-Solvency II markets, to measuring the causal contribution of [[Definition:Fraud detection | fraud detection]] algorithms on recovered [[Definition:Indemnity | indemnity]] amounts. As insurers worldwide invest in causal-inference capabilities, doubly robust methods are becoming a standard component of the analytically rigorous toolkit.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Covariate balance]]
* [[Definition:Counterfactual]]
* [[Definition:Control function approach]]
* [[Definition:Difference-in-differences (DiD)]]
* [[Definition:E-value]]
* [[Definition:Predictive analytics]]
{{Div col end}}

Definition:Doubly robust estimation - Revision history

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