Causal Inference Using Augmented Epidemic Models

TL;DR

This paper explores how to perform causal inference in epidemic models, especially when accounting for interventions and confounders, clarifying the distinction between data-generating and causal models.

Contribution

It introduces a framework for estimating causal effects in augmented epidemic models with time-varying interventions, addressing a key gap in existing modeling approaches.

Findings

Clarified the difference between data-generating and causal epidemic models.

Proposed methods for causal parameter estimation with confounder adjustment.

Applicable to evaluating intervention effects during epidemics.

Abstract

Epidemic models describe the evolution of a communicable disease over time. These models are often modified to include the effects of interventions (control measures) such as vaccination, social distancing, school closings etc. Many such models were proposed during the COVID-19 epidemic. Inevitably these models are used to answer the question: What is the effect of the intervention on the epidemic? These models can either be interpreted as data generating models describing observed random variables or as causal models for counterfactual random variables. These two interpretations are often conflated in the literature. We discuss the difference between these two types of models, and then we discuss how to estimate the parameters of the model. Our focus is causal inference for parameters in epidemic models by adjusting for confounders, allowing time varying interventions.