Information projection approach to propensity score estimation for handling selection bias under missing at random

TL;DR

This paper introduces an information projection method for estimating propensity scores to address selection bias under missing at random, improving efficiency and robustness over traditional models.

Contribution

It proposes a novel density ratio-based approach for propensity score estimation using information projection, enhancing efficiency and handling multivariate missing data.

Findings

Outperforms existing methods in simulations

Achieves efficient propensity score estimation

Handles multivariate missing data effectively

Abstract

Propensity score weighting is widely used to improve the representativeness and correct the selection bias in the voluntary sample. The propensity score is often developed using a model for the sampling probability, which can be subject to model misspecification. In this paper, we consider an alternative approach of estimating the inverse of the propensity scores using the density ratio function satisfying the self-efficiency condition. The smoothed density ratio function is obtained by the solution to the information projection onto the space satisfying the moment conditions on the balancing scores. By including the covariates for the outcome regression models only in the density ratio model, we can achieve efficient propensity score estimation. Penalized regression is used to identify important covariates. We further extend the proposed approach to the multivariate missing case. Some limited simulation studies are presented to compare with the existing methods.