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

Unmeasured confounding bias is among the largest threats to the validity of observational studies. Although sensitivity analyses and various study designs have been proposed to address this issue, they do not leverage the growing availability of auxiliary data accessible through open data platforms. Using negative controls has been introduced in the causal inference literature as a promising approach to account for unmeasured confounding bias. In this paper, we develop a Bayesian nonparametric method to estimate a causal exposure-response function (CERF). This estimation method effectively utilizes auxiliary information from negative control variables to adjust for unmeasured confounding completely. We model the CERF as a mixture of linear models. This strategy offers the dual advantage of capturing the potential nonlinear shape of CERFs while maintaining computational efficiency. Additionally, it leverages closed-form results that hold under the linear model assumption. We assess the performance of our method through simulation studies. The results demonstrate the method's ability to accurately recover the true shape of the CERF in the presence of unmeasured confounding. To showcase the practical utility of our approach, we apply it to adjust for a potential 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 U.S. We implement our estimation procedure in open-source software to ensure transparency and reproducibility and make our code publicly available

Confounder-Dependent Bayesian Mixture Model: Characterizing Heterogeneity of Causal Effects in Air Pollution Epidemiology

Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (PM2.5) increases mortality risk. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify groups of the population that are more or less vulnerable to air pollution. In causal inference literature, the Group Average Treatment Effect (GATE) is a distinctive facet of the conditional average treatment effect. This widely employed metric serves to characterize the heterogeneity of a treatment effect based on some population characteristics. In this work, we introduce a novel Confounder-Dependent Bayesian Mixture Model (CDBMM) to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the dependent Dirichlet process to model the distribution of the potential outcomes conditionally to the covariates and the treatment levels, thus enabling us to: (i) identify heterogeneous and mutually exclusive population groups defined by similar GATEs in a data-driven way, and (ii) estimate and characterize the causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found six mutually exclusive groups where the causal effects of PM2.5 on mortality are heterogeneous