What is doubly robust estimation?
Like many statistical methods, DR estimation addresses the challenge of obtaining unbiased estimates when faced with potential model misspecification, offering protection against errors in either the outcome model or the propensity score model. In causal inference, DR estimation uses two models: the exposure/treatment model, 
, and the outcome model, 
, to ensure accurate results. DR provides reliability even if one model is incorrect.
Let’s dive deeper and learn more about them.
The DR estimator leverages two strengths: accurately modeling the outcome based on covariates (outcome model) and predicting treatment distribution (exposure/treatment model). Remarkably, only one needs to be correct for a reliable causal estimate (we’ll explain why later). This makes the DR estimator a failsafe tool in causal analysis. This dual-model approach is particularly beneficial in complex data analysis, providing a safety net that enhances the reliability...
