Estimating Propensities of Selection for Big Datasets via Data Integration

TL;DR

This paper introduces a new method for estimating selection propensities in big datasets by integrating them with probability samples, improving population inference accuracy amidst selection bias.

Contribution

It proposes a novel propensity score estimation technique leveraging data integration, enhancing bias correction in big data analysis.

Findings

The new method outperforms existing approaches in efficiency.

Data integration improves bias correction accuracy.

Empirical results demonstrate better population estimates.

Abstract

Big data presents potential but unresolved value as a source for analysis and inference. However,selection bias, present in many of these datasets, needs to be accounted for so that appropriate inferences can be made on the target population. One way of approaching the selection bias issue is to first estimate the propensity of inclusion in the big dataset for each member of the big dataset, and then to apply these propensities in an inverse probability weighting approach to produce population estimates. In this paper, we provide details of a new variant of existing propensity score estimation methods that takes advantage of the ability to integrate the big data with a probability sample. We compare the ability of this method to produce efficient inferences for the target population with several alternative methods through an empirical study.