Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
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.
