Interference, the dependency of an individual's potential outcome on the exposure of other individuals, is a common occurrence in medicine and public health. Recently, targeted maximum likelihood estimation (TMLE) has been extended to settings of interference, including in the context of estimation of the mean of an outcome under a specified distribution of exposure, referred to as a policy. This paper summarizes how TMLE for independent data is extended to general interference (network-TMLE). An extensive simulation study is presented of network-TMLE, consisting of four data generating mechanisms (unit-treatment effect only, spillover effects only, unit-treatment and spillover effects, infection transmission) in networks of varying structures. Simulations show that network-TMLE performs well across scenarios with interference, but issues manifest when policies are not well-supported by the observed data, potentially leading to poor confidence interval coverage. Guidance for practical application, freely available software, and areas of future work are provided.

中文翻译：

---

干扰因果效应的目标最大似然估计：模拟研究


干扰，即一个人的潜在结果对其他人的暴露的依赖，在医学和公共卫生领域很常见。最近，目标最大似然估计 (TMLE) 已扩展到干扰环境，包括在指定暴露分布（称为策略）下估计结果平均值的情况。本文总结了独立数据的TMLE如何扩展到一般干扰（网络-TMLE）。对网络 TMLE 进行了广泛的模拟研究，该网络由不同结构的网络中的四种数据生成机制（仅单元处理效应、仅溢出效应、单元处理和溢出效应、感染传播）组成。模拟表明，网络 TMLE 在有干扰的场景中表现良好，但当观察到的数据不能很好地支持策略时，问题就会显现出来，可能导致置信区间覆盖范围很差。提供了实际应用指南、免费软件和未来工作领域。