We propose debiased machine learning estimators for complier parameters, such
as local average treatment effect, with high dimensional covariates. To do so,
we characterize the doubly robust moment function for the entire class of
complier parameters as the combination of Wald and κ weight
formulations. We directly estimate the κ weights, rather than their
components, in order to eliminate the numerically unstable step of inverting
propensity scores of high dimensional covariates. We prove our estimator is
balanced, consistent, asymptotically normal, and semiparametrically efficient,
and use it to estimate the effect of 401(k) participation on the distribution
of net financial assets.Comment: 68 pages, 5 figures, 2 table