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This paper investigates the finite sample performance of a range ofparametric, semi-parametric, and non-parametric instrumental variableestimators when controlling for a fixed set of covariates to evaluate the localaverage treatment effect. Our simulation designs are based on empirical labormarket data from the US and vary in several dimensions, including effectheterogeneity, instrument selectivity, instrument strength, outcomedistribution, and sample size. Among the estimators and simulations considered,non-parametric estimation based on the random forest (a machine learnercontrolling for covariates in a data-driven way) performs competitive in termsof the average coverage rates of the (bootstrap-based) 95% confidenceintervals, while also being relatively precise. Non-parametric kernelregression as well as certain versions of semi-parametric radius matching onthe propensity score, pair matching on the covariates, and inverse probabilityweighting also have a decent coverage, but are less precise than the randomforest-based method. In terms of the average root mean squared error of LATEestimation, kernel regression performs best, closely followed by the randomforest method, which has the lowest average absolute bias.

The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates