Sensitivity analysis for unmeasured pretreatment confounders in causal mediation analysis with debiased machine...

Sensitivity analysis for unmeasured confounders is essential for assessing the robustness of causal mediation conclusions. Most existing methods rely on parametric assumptions, which are ill-suited for machine learning-based estimators that are not tied to specific parametric models. This study develops a sensitivity analysis method for mediation analyses based on the debiased machine learning approach. The proposed method can accommodate categorical (e.g. binary) or continuous mediators and outcomes, quantify robustness to unmeasured pretreatment confounders through contour plots and robustness values defined by $R^2$ -type measures, and allow researchers to avoid parametric assumptions by incorporating data-adaptive machine learning methods. Simulation studies are conducted to evaluate the performance of the proposed method. An empirical example is provided to illustrate the application. We hope this study provides a novel method for quantifying the sensitivity to unmeasured confounding in causal evaluations of mediation effects.