Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding

2026-04-01

AI Summary

This study addresses the identification of natural direct effects in the presence of unmeasured confounding, particularly baseline confounders affecting the exposure–mediator pathway—a setting where conventional approaches rely on the often untenable cross-world counterfactual independence assumption. The authors propose a set of weaker identification conditions that circumvent this assumption, thereby establishing, for the first time in non-randomized vaccine studies, the identifiability of natural direct effects. Leveraging causal mediation analysis and semiparametric efficiency theory, they develop a multiply robust estimator that avoids stringent modeling assumptions on nuisance functions. Applied to real-world vaccine cohort data, the method effectively quantifies the direct causal effect operating through immune-mediated protection, substantially enhancing both the reliability of mechanistic interpretation and estimation efficiency.

Abstract

Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking to address mechanistic questions, causal direct and indirect effects have garnered much attention. The natural direct and indirect effects, the most widely used among such causal mediation estimands, are limited in their practical utility due to stringent identification requirements. Accordingly, considerable effort has been invested in developing alternative direct and indirect effect decompositions with relaxed identification requirements. Such efforts often yield effect definitions with nuanced and challenging interpretations. By contrast, relatively limited attention has been paid to relaxing the identification assumptions of the natural direct and indirect effects. Motivated by a secondary aim of a recent non-randomized vaccine prospective cohort study (NCT05168813), we present a set of relaxed conditions under which the natural direct effect is identifiable in spite of unobserved baseline confounding of the exposure-mediator pathway; we use this result to investigate the effect mediated by putative immune correlates of protection. Relaxing the commonly used but restrictive cross-world counterfactual independence assumption, we discuss strategies for evaluating the natural direct effect in non-randomized settings that arise in the analysis of vaccine studies. We revisit prior studies of semi-parametric efficiency theory to demonstrate the construction of flexible, multiply robust estimators of the natural direct effect and discuss efficient estimation strategies that do not place restrictive modeling assumptions on nuisance functions.

Problem

• causal direct effects
• unmeasured confounding
• natural direct effect
• causal mediation analysis
• identification assumptions

Innovation

• natural direct effect
• unmeasured confounding
• cross-world independence
• multiply robust estimation
• causal mediation analysis