Doubly robust estimation is a statistical approach that combines two methods for estimating treatment effects, ensuring that valid estimates can be obtained even if one of the models used is misspecified. This technique is particularly important in causal inference, where both propensity score modeling and outcome regression are employed to improve the accuracy and reliability of estimates. By leveraging this dual approach, doubly robust estimators can provide more trustworthy results in observational studies.
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