The review explains adaptations of double/debiased machine learning via cross-fitting and Neyman-orthogonal equations for model-assisted estimation and item nonresponse imputation in surveys, while noting that standard inverse-probability weighting remains preferable for unit nonresponse.
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Machine learning methods for finite population parameter estimation in survey sampling
The review explains adaptations of double/debiased machine learning via cross-fitting and Neyman-orthogonal equations for model-assisted estimation and item nonresponse imputation in surveys, while noting that standard inverse-probability weighting remains preferable for unit nonresponse.