  This paper develops an empirical balancing approach for the estimation of
treatment effects under two-sided noncompliance using a binary conditionally
independent instrumental variable. The method weighs both treatment and outcome
information with inverse probabilities to produce exact finite sample balance
across instrument level groups. It is free of functional form assumptions on
the outcome or the treatment selection step. By tailoring the loss function for
the instrument propensity scores, the resulting treatment effect estimates
exhibit both low bias and a reduced variance in finite samples compared to
conventional inverse probability weighting methods. The estimator is
automatically weight normalized and has similar bias properties compared to
conventional two-stage least squares estimation under constant causal effects
for the compliers. We provide conditions for asymptotic normality and
semiparametric efficiency and demonstrate how to utilize additional information
about the treatment selection step for bias reduction in finite samples. The
method can be easily combined with regularization or other statistical learning
approaches to deal with a high-dimensional number of observed confounding
variables. Monte Carlo simulations suggest that the theoretical advantages
translate well to finite samples. The method is illustrated in an empirical
example.
