A Bayesian Nonparametric Method to Adjust for Unmeasured Confounding with Negative Controls

TL;DR

This paper introduces a Bayesian nonparametric approach utilizing negative controls to adjust for unmeasured confounding in observational studies, effectively estimating causal effects with nonlinear relationships.

Contribution

It develops a mixture-of-linear-models Bayesian method that leverages negative controls to correct for unmeasured confounding, with efficient computation and practical software implementation.

Findings

Successfully recovers true CERF shape in simulations

Effectively adjusts for unmeasured confounding in real data

Provides open-source software for reproducibility

Abstract

Unmeasured confounding bias threatens the validity of observational studies. While sensitivity analyses and study designs have been proposed to address this issue, they often overlook the growing availability of auxiliary data. Using negative controls from these data is a promising new approach to reduce unmeasured confounding bias. In this article, we develop a Bayesian nonparametric method to estimate a causal exposure-response function (CERF) leveraging information from negative controls to adjust for unmeasured confounding. We model the CERF as a mixture of linear models. This strategy captures the potential nonlinear shape of CERFs while maintaining computational efficiency, and it leverages closed-form results that hold under the linear model assumption. We assess the performance of our method through simulation studies. We found that the proposed method can recover the true shape of the CERF in the presence of unmeasured confounding under assumptions. To show the practical utility of our approach, we apply it to adjust for a possible unmeasured confounder when evaluating the relationship between long-term exposure to ambient $PM_{2.5}$ and cardiovascular hospitalization rates among the elderly in the continental US. We implement our estimation procedure in open-source software and have made the code publicly available to ensure reproducibility.