Machine learning methods for finite population parameter estimation in survey sampling

TL;DR

This review explores how machine learning enhances finite-population survey inference, addressing challenges in maintaining design-based validity and proposing solutions like cross-fitting and Neyman-orthogonal estimation.

Contribution

It adapts double/debiased machine learning techniques to survey sampling, ensuring valid inference with high-dimensional and nonparametric models.

Findings

Cross-fitting and Neyman-orthogonal estimation enable valid inference with machine learning in survey sampling.

Outcome-agnostic inverse-probability weighting remains operationally attractive for unit nonresponse.

The paper discusses the integration of machine learning in small area estimation and data integration.

Abstract

This pedagogical review examines the use of machine learning methods in finite-population inference for survey sampling, with an emphasis on design-based validity and statistical inference. While flexible prediction tools offer substantial gains in estimation accuracy, they also introduce important challenges, primarily due to the dependence between the fitted predictors and the sample. We focus on settings in which such predictions enter survey estimation through model-assisted estimation, item nonresponse imputation, and unit nonresponse adjustment. For model-assisted estimation and item nonresponse, we show how cross-fitting and Neyman-orthogonal estimating equations can adapt ideas from double/debiased machine learning to survey data, allowing the use of high-dimensional or nonparametric learners while preserving root-n consistency and asymptotic normality under suitable conditions. In contrast, for unit nonresponse, standard inverse-probability weighting remains outcome-agnostic and operationally attractive, but this same feature makes doubly robust and orthogonal constructions harder to deploy in official statistics. We also briefly discuss related developments in small area estimation and probability/nonprobability data integration. Overall, the paper highlights both the promise of machine learning and the fundamental inferential challenges it raises for survey practice.