On calibrated inverse probability weighting and generalized boosting propensity score models for mean estimation with incomplete survey data

Abstract

AbstractIncomplete data, whether realized from nonresponse in survey data or counterfactual outcomes in observational studies, may lead to biased estimation of study variables. Nonresponse and selection bias may be mitigated with techniques that weight the incomplete data to match characteristics of the partially unobserved complete data. Inverse probability weighting is a widely used method in causal inference that relies on a propensity model to construct adjusted weights; whereas calibration is a common method used by survey statisticians to use constrained optimization to construct adjusted weights. This article reviews inverse probability weighting and entropy balancing calibration by distinguishing them in the statistical sense of variable balancing, extending propensity score construction to include generalized boosting models, and demonstrating the use of inverse probability weighting and calibration separately and together through a widely cited simulation study evaluation.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Sampling Data: Types and Structure > Categorical Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods