  Despite increasing popularity in empirical studies, the integration of
machine learning generated variables into regression models for statistical
inference suffers from the measurement error problem, which can bias estimation
and threaten the validity of inferences. In this paper, we develop a novel
approach to alleviate associated estimation biases. Our proposed approach,
EnsembleIV, creates valid and strong instrumental variables from weak learners
in an ensemble model, and uses them to obtain consistent estimates that are
robust against the measurement error problem. Our empirical evaluations, using
both synthetic and real-world datasets, show that EnsembleIV can effectively
reduce estimation biases across several common regression specifications, and
can be combined with modern deep learning techniques when dealing with
unstructured data.
