论文标题
具有稳定估计器的样品分解的机器学习
Debiased Machine Learning without Sample-Splitting for Stable Estimators
论文作者
论文摘要
对因果参数的估计和推断通常会简化为矩问题的广义方法，该方法涉及与回归或分类问题的解决方案相对应的辅助功能。最新的关于依据的机器学习的工作表明，人们如何在这些辅助问题上使用通用的机器学习估计器，同时维持感兴趣的目标参数的渐近正态性和根源 - $ n $一致性，而仅需要从辅助估计算法中进行均值率保证。文献通常要求将这些辅助问题安装在单独的样本或交叉方式上。我们表明，当这些辅助估计算法满足自然保留的稳定性时，不需要样品分裂。这允许重新使用样品，这在适度大小的样本方案中可能是有益的。例如，我们表明，我们建议的稳定性对集合装袋的估计量满足，这是通过无替换的子采样构建的，这是机器学习实践中一种流行的技术。
Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line of work on debiased machine learning shows how one can use generic machine learning estimators for these auxiliary problems, while maintaining asymptotic normality and root-$n$ consistency of the target parameter of interest, while only requiring mean-squared-error guarantees from the auxiliary estimation algorithms. The literature typically requires that these auxiliary problems are fitted on a separate sample or in a cross-fitting manner. We show that when these auxiliary estimation algorithms satisfy natural leave-one-out stability properties, then sample splitting is not required. This allows for sample re-use, which can be beneficial in moderately sized sample regimes. For instance, we show that the stability properties that we propose are satisfied for ensemble bagged estimators, built via sub-sampling without replacement, a popular technique in machine learning practice.
