Nonparametric inverse-probability-weighted estimators based on the highly adaptive lasso.

ABSTRACT: Inverse-probability-weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biases via a weighting mechanism, these procedures estimate an effect of interest by constructing a pseudopopulation in which selection biases are eliminated. Despite their ease of use, these estimators require the correct specification of a model for the weighting mechanism, are known to be inefficient, and suffer from the curse of dimensionality. We propose a class of nonparametric inverse-probability-weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at nearly n-1/3$ n^{-1/3}$ -rate to the true wei

SUBMITTER: Ertefaie A 

PROVIDER: S-EPMC9840713 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature