A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

Abstract: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso (OAL) can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using the OAL was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the OAL to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the OAL for PS models and various outcome-oriented covariate selection via penalization for outcome models. We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. The performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was high. In a clinical trial dataset analysis, the AIPW estimators using outcome-oriented covariate selection via penalization with and without the oracle property showed similar estimates and standard errors.