Robust Estimation and Model Selection for the Controlled Directed Effect with Unmeasured Mediator-Outcome Confounders

TL;DR

This paper introduces a robust estimation method and a model selection criterion for the Controlled Direct Effect in causal inference, addressing unmeasured confounders using instrumental variables and demonstrating effectiveness through simulations and real data.

Contribution

It proposes a novel doubly robust estimator for CDE with unmeasured confounders and a GIC-based model selection criterion ensuring consistency.

Findings

The method accurately estimates CDE in simulations.

The GIC criterion effectively selects the correct model.

Application to real data confirms practical utility.

Abstract

Controlled Direct Effect (CDE) is one of the causal estimands used to evaluate both exposure and mediation effects on an outcome. When there are unmeasured confounders existing between the mediator and the outcome, the ordinary identification assumption does not work. In this manuscript, we consider an identification condition to identify CDE in the presence of unmeasured confounders. The key assumptions are: 1) the random allocation of the exposure, and 2) the existence of instrumental variables directly related to the mediator. Under these conditions, we propose a novel doubly robust estimation method, which work well if either the propensity score model or the baseline outcome model is correctly specified. Additionally, we propose a Generalized Information Criterion (GIC)-based model selection criterion for CDE that ensures model selection consistency. Our proposed procedure and related methods are applied to both simulation and real datasets to confirm the performance of these methods. Our proposed method can select the correct model with high probability and accurately estimate CDE.