Under a null-effect plasmode simulation, outcome-adaptive LASSO (IPTW), GLiDeR, and HAL-TMLE were best calibrated across frequent, rare-exposure, and rare-outcome scenarios, while LASSO-IPTW was biased under rare exposure and HAL G-computation over-covered.
How effective are machine learning and doubly robust estimators in incorporating high-dimensional proxies to reduce residual confounding? Pharmacoepidemiology and Drug Safety
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Which Regularized Propensity-Score and Doubly Robust Methods Are Best Calibrated When Exposures or Outcomes Are Rare? A Plasmode Study of Proxy-Based Confounding Adjustment
Under a null-effect plasmode simulation, outcome-adaptive LASSO (IPTW), GLiDeR, and HAL-TMLE were best calibrated across frequent, rare-exposure, and rare-outcome scenarios, while LASSO-IPTW was biased under rare exposure and HAL G-computation over-covered.