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.

翻译：流行病模型描述了传染病随时间的演变过程。这些模型通常经过修改以纳入干预措施（控制手段）的影响，例如疫苗接种、社交距离、学校关闭等。在COVID-19大流行期间提出了许多此类模型。这些模型不可避免地用于回答以下问题：干预措施对疫情有何影响？这些模型既可以解释为描述观测随机变量的数据生成模型，也可以解释为反事实随机变量的因果模型。在现有文献中，这两种解释常被混淆。我们讨论了这两类模型之间的区别，随后探讨了如何估计模型参数。我们的重点是通过调整混杂因素、允许时变干预，实现流行病模型参数的因果推断。