Unveiling Causal Mediation Pathways in High-Dimensional Mixed Exposures: A Data-Adaptive Target Parameter Strategy

TL;DR

This paper introduces NOVAPathways, a flexible framework for identifying and estimating causal mediation effects in high-dimensional, mixed exposure data using data-adaptive methods and machine learning, advancing mediation analysis beyond traditional single-exposure models.

Contribution

It develops a semi-parametric, data-adaptive approach for uncovering exposure-mediation pathways and estimating direct and indirect effects in complex high-dimensional settings with continuous and categorical variables.

Findings

Demonstrates asymptotic linearity of the estimator under certain conditions.

Shows square root n consistency with quantized exposure data.

Provides an open-source R package for practical implementation.

Abstract

Mediation analysis in causal inference typically concentrates on one binary exposure, using deterministic interventions to split the average treatment effect into direct and indirect effects through a single mediator. Yet, real-world exposure scenarios often involve multiple continuous exposures impacting health outcomes through varied mediation pathways, which remain unknown a priori. Addressing this complexity, we introduce NOVAPathways, a methodological framework that identifies exposure-mediation pathways and yields unbiased estimates of direct and indirect effects when intervening on these pathways. By pairing data-adaptive target parameters with stochastic interventions, we offer a semi-parametric approach for estimating causal effects in the context of high-dimensional, continuous, binary, and categorical exposures and mediators. In our proposed cross-validation procedure, we apply sequential semi-parametric regressions to a parameter-generating fold of the data, discovering exposure-mediation pathways. We then use stochastic interventions on these pathways in an estimation fold of the data to construct efficient estimators of natural direct and indirect effects using flexible machine learning techniques. Our estimator proves to be asymptotically linear under conditions necessitating n to the negative quarter consistency of nuisance function estimation. Simulation studies demonstrate the square root n consistency of our estimator when the exposure is quantized, whereas for truly continuous data, approximations in numerical integration prevent square root n consistency. Our NOVAPathways framework, part of the open-source SuperNOVA package in R, makes our proposed methodology for high-dimensional mediation analysis available to researchers, paving the way for the application of modified exposure policies which can delivery more informative statistical results for public policy.