Abstract We study the framework for semi-parametric estimation and statistical inference for the sample average treatment-specific mean effects in observational settings where data are collected on a single network of possibly dependent units (e.g., in the presence of interference or spillover). Despite recent advances, many of the current statistical methods rely on estimation techniques that assume a particular parametric model for the outcome, even though some of the important statistical assumptions required by these methods are often violated in observational network settings. In this work we rely on recent methodological advances in the field of targeted maximum likelihood estimation (TMLE) and describe an estimation approach that permits for more realistic classes of data-generative models while providing valid inference in the context of observational network-dependent data. We start by assuming that the true data-generating distribution belongs to a large class of semi-parametric statistical models. We then impose some restrictions on the possible set of such distributions. For example, we assume that the dependence among the observed outcomes can be fully described by an observed network. We then show that under our modeling assumptions, our estimand can be described as a functional of the mixture of the observed data-generating distribution. With this key insight in mind, we describe the TMLE for possibly-dependent units as an iid data algorithm and we demonstrate the validity of our approach with a simulation study. Finally, we extend prior work towards estimation of novel causal parameters such as the unit-specific indirect and direct treatment effects under interference and the effects of interventions that modify the structure of the network.

中文翻译：

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因果相关群体中单一时间点干预平均结果的半参数估计和推断

摘要 我们研究了半参数估计和统计推断的框架，用于观察环境中样本平均处理特定的平均效应，其中数据是在可能依赖的单元的单个网络上收集的（例如，在存在干扰或溢出的情况下）。尽管最近取得了进展，但许多当前的统计方法依赖于为结果假设特定参数模型的估计技术，即使这些方法所需的一些重要统计假设在观测网络设置中经常被违反。在这项工作中，我们依靠目标最大似然估计 (TMLE) 领域的最新方法学进展，并描述了一种估计方法，该方法允许更现实的数据生成模型类别，同时在观测网络相关数据的上下文中提供有效推理。我们首先假设真正的数据生成分布属于一大类半参数统计模型。然后我们对可能的此类分布集施加一些限制。例如，我们假设观察到的结果之间的依赖性可以由观察到的网络完全描述。然后我们表明，在我们的建模假设下，我们的估计量可以描述为观察到的数据生成分布的混合函数。考虑到这一关键见解，我们将可能依赖单元的 TMLE 描述为 iid 数据算法，并通过模拟研究证明了我们方法的有效性。最后，我们将先前的工作扩展到估计新的因果参数，例如干扰下特定于单元的间接和直接治疗效果以及修改网络结构的干预效果。