Model misspecification and bias for inverse probability weighting estimators of average causal effects.

ABSTRACT: Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating proces

SUBMITTER: Waernbaum I 

PROVIDER: S-EPMC10087564 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature