# An approximate Bayesian inference on propensity score estimation under unit nonresponse

## TL;DR

This paper introduces an approximate Bayesian approach for propensity score estimation under unit nonresponse, providing a Bayesian inference method that aligns with frequentist confidence intervals without requiring Taylor linearization.

## Contribution

It proposes a novel Bayesian method for nonresponse adjustment that avoids Taylor linearization and can handle over-identified and nonignorable nonresponse cases.

## Key findings

- Simulation studies confirm the method's validity.
- The approach aligns Bayesian credible regions with frequentist confidence intervals.
- Application to survey data demonstrates practical utility.

## Abstract

Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the nonresponse weighting adjustment is complicated because the effect of estimating the propensity model parameter needs to be incorporated. In this paper, we propose an approximate Bayesian approach to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is calibrated to the frequentist inference in that the credible region obtained from the posterior distribution asymptotically matches to the frequentist confidence interval obtained from the Taylor linearization method. Unlike the frequentist approach, however, the proposed method does not involve Taylor linearization. The proposed method can be extended to handle over-identified cases in which there are more estimating equations than the parameters. Besides, the proposed method can also be modified to handle nonignorable nonresponse. Results from two simulation studies confirm the validity of the proposed methods, which are then applied to data from a Korean longitudinal survey.