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 $\kappa$ weight formulations. We directly estimate the $\kappa$ 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.

翻译：我们建议对符合标准参数,例如具有高维共差的当地平均治疗效应等,进行偏差的机器学习估计;为此,我们把整个类别的遵守标准参数的双强时空功能定性为Wald和$\kappa美元重量配方的组合。我们直接估计$\kappa$的重量,而不是其组成部分,以便消除在数字上不稳定的反向高维共差分数的步骤。我们证明我们的遵守标准是平衡的、一贯的、无干扰的正常的和半对称效率的,并用它来估计401(k)参与净金融资产分配的影响。