  We offer straightforward theoretical results that justify incorporating
machine learning in the standard linear instrumental variable setting. The key
idea is to use machine learning, combined with sample-splitting, to predict the
treatment variable from the instrument and any exogenous covariates, and then
use this predicted treatment and the covariates as technical instruments to
recover the coefficients in the second-stage. This allows the researcher to
extract non-linear co-variation between the treatment and instrument that may
dramatically improve estimation precision and robustness by boosting instrument
strength. Importantly, we constrain the machine-learned predictions to be
linear in the exogenous covariates, thus avoiding spurious identification
arising from non-linear relationships between the treatment and the covariates.
We show that this approach delivers consistent and asymptotically normal
estimates under weak conditions and that it may be adapted to be
semiparametrically efficient (Chamberlain, 1992). Our method preserves standard
intuitions and interpretations of linear instrumental variable methods,
including under weak identification, and provides a simple, user-friendly
upgrade to the applied economics toolbox. We illustrate our method with an
example in law and criminal justice, examining the causal effect of appellate
court reversals on district court sentencing decisions.
