The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

ABSTRACT: Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two

SUBMITTER: Setodji CM 

PROVIDER: S-EPMC5617809 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature