  Survey scientists increasingly face the problem of high-dimensionality in
their research as digitization makes it much easier to construct
high-dimensional (or "big") data sets through tools such as online surveys and
mobile applications. Machine learning methods are able to handle such data, and
they have been successfully applied to solve \emph{predictive} problems.
However, in many situations, survey statisticians want to learn about
\emph{causal} relationships to draw conclusions and be able to transfer the
findings of one survey to another. Standard machine learning methods provide
biased estimates of such relationships. We introduce into survey statistics the
double machine learning approach, which gives approximately unbiased estimators
of causal parameters, and show how it can be used to analyze survey nonresponse
in a high-dimensional panel setting.
