On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding

TL;DR

This paper develops semiparametric estimators for path-specific effects in causal inference, relaxing traditional assumptions and demonstrating robustness and efficiency through simulations and an HIV treatment study.

Contribution

It introduces new semiparametric estimators for path-specific effects that are robust and efficient, addressing confounding issues in causal pathway analysis.

Findings

Estimators are locally semiparametric efficient.

Estimators exhibit multiple robustness properties.

Application to HIV study demonstrates practical utility.

Abstract

Path-specific effects are a broad class of mediated effects from an exposure to an outcome via one or more causal pathways with respect to some subset of intermediate variables. The majority of the literature concerning estimation of mediated effects has focused on parametric models with stringent assumptions regarding unmeasured confounding. We consider semiparametric inference of a path-specific effect when these assumptions are relaxed. In particular, we develop a suite of semiparametric estimators for the effect along a pathway through a mediator, but not some exposure-induced confounder of that mediator. These estimators have different robustness properties, as each depends on different parts of the observed data likelihood. One of our estimators may be viewed as combining the others, because it is locally semiparametric efficient and multiply robust. The latter property is illustrated in a simulation study. We apply our methodology to an HIV study, in which we estimate the effect comparing two drug treatments on a patient's average log CD4 count mediated by the patient's level of adherence, but not by previous experience of toxicity, which is clearly affected by which treatment the patient is assigned to, and may confound the effect of the patient's level of adherence on their virologic outcome.