Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples

Nonprobability samples emerge rapidly to address time -sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity -score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability -based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS -based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS -based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01 - August 04, 2020.