Functional Causal Mediation Analysis with Zero-inflated Count Data

Music City Center 

Mediation analysis is crucial for understanding how a treatment exerts effects on an outcome via an intermediate variable, known as the mediator. Zero-inflated count outcomes and time-varying mediators are prevalent in fields such as biomedicine and biostatistics. To address the complex structure of the data, we extend existing mediation analysis methodologies by integrating a functional mediator in the context of zero-inflated count outcomes. The potential outcomes framework is employed to define the mediation effects of interest in this context and to provide the theoretical underpinning for our approach, including conditions for effect identification. Estimation and inference on the direct and indirect effects are performed by a quasi-Bayesian Monte Carlo approximation method using the well-known mediation formula. The methods are applied to study gender disparity in the number of re-admission to ICU for patients in MIMIC-IV, an electronic health record database.

Causal inference

Count data

Functional data analysis

Mediation analysis

Zero-inflation