Measuring Model Misspecification: Application to Propensity Score Methods with Complex Survey Data.

ABSTRACT: Model misspecification is a potential problem for any parametric-model based analysis. However, the measurement and consequences of model misspecification have not been well formalized in the context of causal inference. A measure of model misspecification is proposed, and the consequences of model misspecification in non-experimental causal inference methods are investigated. The metric is then used to explore which estimators are more sensitive to misspecification of the outcome and/or treatment assignment model. Three frequently used estimators of the treatment effect are considered, all of which rely on the propensity score: (1) full matching, (2) 1:1 nearest neighbor matching, and (3) weighting. The performance of these estimators is evaluated under two different sampling designs: (1)

SUBMITTER: Lenis D 

PROVIDER: S-EPMC6034692 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature