Design-unbiased statistical learning in survey sampling

Approximately design-unbiased model-assisted estimation is not new. It has become the standard practice in survey sampling, following many influential works such as Särndal et al. (1992), Deville and Särndal (1992). However, there lacks so far a theory, which allows one to generally incorporate the many common machine-learning (ML) techniques. For instance, according to Breit and Opsomer(2017, p. 203), they"are not aware of direct uses of random forests in a model-assisted survey estimator". Since modern ML techniques can often generate more flexible and powerful prediction models, when rich auxiliary feature data are available, the potentials are worth exploring, in any situation where the practical advantages of linear weighting are not essential compared to the efficiency gains that can be achieved by alternative nonlinear ML techniques. We propose a subsampling Rao-Blackwell(SRB) method, which enables exactly designunbiased estimation with the help of linear or nonlinear prediction models. Monte Carlo (MC) versions of the proposed method can be used in cases where exact RB method is computationally too costly.