Estimation of average treatment effect based on a multi-index propensity score.

ABSTRACT:

Background

Estimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to estimate the average treatment effect (ATE). In this study, we proposed an estimator to correct confounding bias and provide multiple protection for estimation consistency.

Methods

With reference to the kernel function-based double-index propensity score (Ker.DiPS) estimator, we proposed the artificial neural network-based multi-index propensity score (ANN.MiPS) estimator. The ANN.MiPS estimator employed the artificial neural network to estimate the MiPS that combines the information from multiple candidate models for propensit

SUBMITTER: Xu J 

PROVIDER: S-EPMC9795597 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature