A0219
Title: A debiased estimator for the mediation functional in ultra-high-dimensional setting with interaction effects
Authors: AmirEmad Ghassami - Boston University (United States)
Debarghya Mukherjee - Boston University (United States) [presenting]
Shi Bo - Boston University (United States)
Abstract: Mediation analysis is crucial in many fields of science for understanding the mechanisms or processes through which an independent variable affects an outcome, thereby providing deeper insights into causal relationships and improving intervention strategies. Despite advances in analyzing the mediation effect with fixed/low-dimensional mediators and covariates, the understanding of estimation and inference of mediation functional in the presence of (ultra)-high-dimensional mediators and covariates is still limited. An estimator is presented for mediation functional in a high-dimensional setting that accommodates the interaction between covariates and treatment in generating mediators, as well as interactions between both covariates and treatment and mediators and treatment in generating the response. It is demonstrated that the estimator is root(n)-consistent and asymptotically normal, thus enabling reliable inference on direct and indirect treatment effects with asymptotically valid confidence intervals. A key technical contribution of the work is to develop a multi-step debiasing technique, which may also be valuable in other statistical settings with similar structural complexities where accurate estimation depends on debiasing. Furthermore, findings are supported by extensive simulation and understanding of the effect of smoking on lung cancer, mediated through DNA methylation.
