Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention.

中文翻译：

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网络化治疗扩散的因果推断

治疗干扰（即一个单位的潜在结果取决于其他单位的治疗）在社会环境中很普遍。忽略治疗干扰会导致对治疗效果的估计有偏差和不正确的统计推断。最近的一些研究开始将治疗干扰纳入因果推断。但治疗干扰通常被假定遵循一个简单的结构（例如，治疗干扰只存在于群体内）或以简单的方式衡量（例如，仅基于被治疗的朋友的数量）。在本文中，我强调收集实际治疗扩散数据的重要性，以便更准确地衡量治疗干扰。此外，我表明，通过对治疗干扰的准确测量，我们可以识别和估计一系列以前无法获得的因果效应，包括直接治疗效应、治疗干扰效应和治疗对干扰的影响。我通过一个基于社交网络的吸烟预防干预案例研究来说明这些方法。