A1084
Title: Proximal inference on population intervention indirect effect
Authors: Yang Bai - National University of Singapore (Singapore) [presenting]
Yifan Cui - Zhejiang University (China)
BaoLuo Sun - National University of Singapore (Singapore)
Abstract: The population intervention indirect effect (PIIE) is a novel mediation effect representing the indirect component of the population intervention effect. Unlike traditional mediation measures, such as the natural indirect effect, the PIIE holds particular relevance in observational studies involving unethical exposures when hypothetical interventions that impose harmful exposures are inappropriate. Although prior research has identified PIIE under unmeasured confounders between exposure and outcome, it has not fully addressed the confounding that affects the mediator. The purpose is to extend the PIIE identification to settings where unmeasured confounders influence exposure-outcome, exposure-mediator, and mediator-outcome relationships. Specifically, observed covariates are leveraged as proxy variables for unmeasured confounders, constructing three proximal identification frameworks. Additionally, the semiparametric efficiency bound is characterized, and multiple robust and locally efficient estimators are developed. To handle high-dimensional nuisance parameters, a debiased machine learning approach is proposed that achieves $\sqrt{n}$-consistency and asymptotic normality to estimate the true PIIE values, even when the machine learning estimators for the nuisance functions do not converge at $\sqrt{n}$-rate. The validity of the approaches is demonstrated by simulations and a real data application.
