  This paper investigates the finite sample performance of a range of
parametric, semi-parametric, and non-parametric instrumental variable
estimators when controlling for a fixed set of covariates to evaluate the local
average treatment effect. Our simulation designs are based on empirical labor
market data from the US and vary in several dimensions, including effect
heterogeneity, instrument selectivity, instrument strength, outcome
distribution, and sample size. Among the estimators and simulations considered,
non-parametric estimation based on the random forest (a machine learner
controlling for covariates in a data-driven way) performs competitive in terms
of the average coverage rates of the (bootstrap-based) 95% confidence
intervals, while also being relatively precise. Non-parametric kernel
regression as well as certain versions of semi-parametric radius matching on
the propensity score, pair matching on the covariates, and inverse probability
weighting also have a decent coverage, but are less precise than the random
forest-based method. In terms of the average root mean squared error of LATE
estimation, kernel regression performs best, closely followed by the random
forest method, which has the lowest average absolute bias.
