We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning (Auto-DML). By regularizing the balancing weights, it does not require ad hoc trimming or censoring. We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. We use the new approach to estimate the effect of 401(k) participation on the distribution of net financial assets.
翻译:我们采用了一种消除偏见的机器学习(DML)方法来估计高维数据的遵守者参数;兼容的参数包括当地平均处理效果、平均遵守者特征和遵守者反事实结果分布;在我们的方法中,减少偏见本身是通过机器学习(Auto-DML)来进行的,一种称为自动消除偏见的机器学习(Auto-DML)的变体。通过调整平衡权重,它不需要特别的裁剪或审查。我们证明我们的估量者是一致的,不那么正常的,半对称的效率。我们用新的方法来估计401(k)参与对净金融资产分配的影响。