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Causal mediation analysis: selection with asymptotically valid inference.

Jeremiah Jones^1,2, Ashkan Ertefaie¹, Robert L Strawderman¹

¹Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology

|July 15, 2025

Summary

This summary is machine-generated.

This study introduces a new penalized mediation analysis method to identify key mediators. It addresses limitations of existing approaches by controlling for confounding bias and improving mediator selection accuracy.

Keywords:

adaptive penalty mediation analysis partial linear model variable selection

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Area of Science:

Biostatistics
Epidemiology
Statistical Genetics

Background:

Understanding treatment effects requires identifying mediating variables.
Existing penalized mediation methods may overlook crucial mediators and fail to adequately control for confounding bias.
Assumptions of finite-dimensional linear models in current methods may not hold in practice.

Purpose of the Study:

Propose a novel penalized mediation analysis method to accurately identify important mediators.
Address limitations of existing methods by incorporating data-adaptive estimation of confounding functions.
Estimate natural direct and indirect effects while controlling for confounding bias.

Main Methods:

Develop a data-adaptive approach to estimate confounding functions as nuisance parameters.
Apply a novel regularization technique to identify significant mediators.
Derive asymptotic properties of the proposed estimators, including the oracle property.
Utilize a perturbation bootstrap for asymptotically valid postselection inference.

Main Results:

The proposed method effectively identifies important mediators while controlling for confounding.
Asymptotic results demonstrate the oracle property under specified assumptions.
Local asymptotic results offer a contrast to standard adaptive lasso methods.
Perturbation bootstrap provides reliable inference for mediated effects postselection.

Conclusions:

The novel penalized mediation analysis offers a robust approach for identifying mediators and estimating effects.
This method improves upon existing techniques by effectively handling confounding bias.
The proposed techniques provide a valuable tool for researchers investigating complex causal pathways.