Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments

Houssam Zenati, Judith Abécassis, Julie Josse, Bertrand Thirion
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:4150-4158, 2025.

Abstract

Uncovering causal mediation effects is of significant value to practitioners who aim to isolate treatment effects from potential mediator effects. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the target mediated response curve, our method employs a kernel-based doubly robust moment function for which we prove asymptotic Neyman orthogonality. This allows us to obtain an asymptotic normality with nonparametric convergence rate while allowing for nonparametric or parametric estimation of the nuisance parameters. Subsequently, we derive an optimal bandwidth strategy along with a procedure to estimate 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 on medical real-world data to analyze the effect of glycemic control on cognitive functions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-zenati25a, title = {Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments}, author = {Zenati, Houssam and Ab{\'e}cassis, Judith and Josse, Julie and Thirion, Bertrand}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {4150--4158}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/zenati25a/zenati25a.pdf}, url = {https://proceedings.mlr.press/v258/zenati25a.html}, abstract = {Uncovering causal mediation effects is of significant value to practitioners who aim to isolate treatment effects from potential mediator effects. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the target mediated response curve, our method employs a kernel-based doubly robust moment function for which we prove asymptotic Neyman orthogonality. This allows us to obtain an asymptotic normality with nonparametric convergence rate while allowing for nonparametric or parametric estimation of the nuisance parameters. Subsequently, we derive an optimal bandwidth strategy along with a procedure to estimate 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 on medical real-world data to analyze the effect of glycemic control on cognitive functions.} }
Endnote
%0 Conference Paper %T Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments %A Houssam Zenati %A Judith Abécassis %A Julie Josse %A Bertrand Thirion %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-zenati25a %I PMLR %P 4150--4158 %U https://proceedings.mlr.press/v258/zenati25a.html %V 258 %X Uncovering causal mediation effects is of significant value to practitioners who aim to isolate treatment effects from potential mediator effects. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the target mediated response curve, our method employs a kernel-based doubly robust moment function for which we prove asymptotic Neyman orthogonality. This allows us to obtain an asymptotic normality with nonparametric convergence rate while allowing for nonparametric or parametric estimation of the nuisance parameters. Subsequently, we derive an optimal bandwidth strategy along with a procedure to estimate 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 on medical real-world data to analyze the effect of glycemic control on cognitive functions.
APA
Zenati, H., Abécassis, J., Josse, J. & Thirion, B.. (2025). Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:4150-4158 Available from https://proceedings.mlr.press/v258/zenati25a.html.

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