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.
翻译:本文建议使用名为“双分匹配”(DSM)的方法进行批量估计,并应用该方法对非概率抽样进行推断。 DSM是一种使用两个平衡分数而不是共差来降低距离衡量尺度的维度,从而实现更快的趋同率的计算法。 DSM 质量估计和人口推断是一致的,如果对两个平衡分数模型中的一个进行正确指定的话。模拟结果显示,当数据生成过程有非线性相近者时,DSM比最近开发的双强估计值表现得更好。 DGP的非线性是一个主要关切,因为它无法测试,导致违反实现一致性所需的假设。即使DSM的一致性依赖于两个模型假设,它也防止在这类情况下出现增缩偏差,因为DSM是一个半参数估测器。信任度间隔是用野生靴杆制方法构建的。拟议的制靴式方法在DSMM(DSM)保持一致时产生有效的信任度间隔。