Abstract : We deal with the practical construction of confidence intervals (CIs) for a
real-valued, smooth parameter by targeted learning, when sample size is so
large that the resulting computational problems cannot be skirted. We propose
to carry out targeted learning on a sub-sample selected with unequal inclusion
probabilities based on easy to observe summary measures of the data. As
examples, we show how to use Sampford's and determinantal survey sampling
designs. The inclusion probabilities can be optimized to the reduce the width
of the CIs. A simulation study illustrates our results.