论文标题
协调双机器学习
Coordinated Double Machine Learning
论文作者
论文摘要
双机器学习是一种统计方法，用于利用复杂的黑盒模型在某些线性模型的假设下构建具有高维协变量的观察数据，并在具有高维协变量的观察数据中构建近似公正的治疗效果估计。这个想法是首先拟合两个非线性预测模型的子集，一个用于连续的结果，一种用于观察到的处理，然后通过简单的正交回归估算使用其余样品的处理线性系数。尽管这种方法是灵活的，并且可以容纳任意预测模型，通常是彼此独立训练的，但本文认为，针对深层神经网络的精心协调的学习算法可能会减少估计偏差。通过模拟和真实数据的数值实验证明了所提出方法的经验性能的改善。
Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model. The idea is to first fit on a subset of the samples two non-linear predictive models, one for the continuous outcome of interest and one for the observed treatment, and then to estimate a linear coefficient for the treatment using the remaining samples through a simple orthogonalized regression. While this methodology is flexible and can accommodate arbitrary predictive models, typically trained independently of one another, this paper argues that a carefully coordinated learning algorithm for deep neural networks may reduce the estimation bias. The improved empirical performance of the proposed method is demonstrated through numerical experiments on both simulated and real data.
