Double Robust Mass-Imputation with Matching Estimators

TL;DR

This paper introduces Double Score Matching (DSM), a double robust mass-imputation method using two balance scores to improve inference from nonprobability samples, especially under nonlinear confounders.

Contribution

The paper proposes DSM, a novel k-NN based double robust estimator that reduces dimensionality with two scores, enhancing inference accuracy in complex data scenarios.

Findings

DSM outperforms recent double robust estimators in simulations.

DSM maintains consistency if one of two models is correct.

Wild bootstrap provides valid confidence intervals for DSM.

Abstract

This paper proposes using a method named Double Score Matching (DSM) to do mass-imputation and presents an application to make inferences with a nonprobability sample. DSM is a $k$-Nearest Neighbors algorithm that uses two balance scores instead of covariates to reduce the dimension of the distance metric and thus to achieve a faster convergence rate. DSM mass-imputation and population inference are consistent if one of two balance score models is correctly specified. Simulation results show that the DSM performs better than recently developed double robust estimators when the data generating process has nonlinear confounders. The nonlinearity of the DGP is a major concern because it cannot be tested, and it leads to a violation of the assumptions required to achieve consistency. Even if the consistency of the DSM relies on the two modeling assumptions, it prevents bias from inflating under such cases because DSM is a semiparametric estimator. The confidence intervals are constructed using a wild bootstrapping approach. The proposed bootstrapping method generates valid confidence intervals as long as DSM is consistent.