提出连续处理的双重机器学习中介分析法，可精准分离直接与中介效应。
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
- 基于核方法构建双重稳健矩函数，实现非参数估计
- 理论证明渐近正态性，收敛速度达非参数水平
- 适用于医学数据中血糖控制对认知功能影响分析
揭示因果中介效应对希望分离直接处理效应与潜在中介效应的实践者具有重要意义。我们提出一种支持连续处理的双重机器学习（DML）中介分析算法。为估计目标中介响应曲线，该方法采用基于核的双重稳健矩函数，并证明其满足渐近Neyman正交性。这使得在允许对干扰参数进行非参数或参数估计的同时，仍能获得渐近正态性及非参数收敛率。随后，我们推导出最优带宽策略，并给出渐近置信区间估计方法。最后，通过模拟实验和真实医疗数据应用，验证了本方法在分析血糖控制对认知功能影响中的有效性。
原文摘要 · Abstract (English)
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the target mediated response curve, our method uses a kernel-based doubly robust moment function for which we prove asymptotic Neyman orthogonality. This allows us to obtain asymptotic normality with nonparametric convergence rate while allowing for nonparametric or parametric estimation of the nuisance parameters. We then derive an optimal bandwidth strategy along with a procedure for estimating asymptotic confidence intervals. Finally, to illustrate the benefits of our method, we provide a numerical evaluation of our approach on a simulation along with an application to real-world medical data to analyze the effect of glycemic control on cognitive functions.
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