Am J Epidemiol. 2021 Aug 1;190(8):1652-1658. doi: 10.1093/aje/kwab040.
Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key "no interference" assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.
基于代理的模型是研究人群健康环境中涌现属性（如传染病传播）的关键工具，在潜在结果框架下，这种暴露通常违反了传统因果推断的关键“无干扰”假设。基于代理的模型和其他基于模拟的建模方法通常被视为与潜在结果框架不同的知识生成范式，但这可能导致人们混淆如何在实际环境中解释这些模型的结果。通过将目标试验框架明确纳入基于代理的模型或其他模拟模型的开发中，我们可以澄清感兴趣的因果参数，并明确在人群内或人群间进行有效因果效应估计所需的假设。在本文中，当目标是在存在干扰或溢出的情况下估计因果效应时，我们描述了使用目标试验框架来设计基于代理的模型。
