Halloran M E, Struchiner C J
Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
Epidemiology. 1995 Mar;6(2):142-51. doi: 10.1097/00001648-199503000-00010.
Since the 1970s, Rubin has promoted a model for causal inference based on the potential outcomes if individuals received each of the treatments under study. Commonly, the assumption is made that the outcome in one individual is independent of the treatment assignment and outcome in other individuals. In infectious diseases, however, whether one person become infected is quite often dependent on the infection outcome in other individuals, a situation known as dependent happenings. Here, we review the model proposed by Rubin for the example of infectious disease. Consequences of the violation of the stability assumption include the need for an expanded representation of outcomes, and the existence of different kinds of effects, such as direct and indirect effects. Effects of interest include changes in susceptibility as well as changes in infectiousness. We define the transmission probability formally as an average causal parameter of effect in a population by conditioning on exposure to infection. Unconditional indirect and total effects are difficult to define formally using this model for causal inference. The assignment mechanism can influence the sampling mechanism when it determines who is exposed to infection, raising problems that require further inquiry. We conclude by contrasting the role of differential exposure to infection in direct and indirect effects.
自20世纪70年代以来，鲁宾提出了一种因果推断模型，该模型基于个体接受每种研究中的治疗时的潜在结果。通常情况下，假设一个个体的结果与其他个体的治疗分配和结果无关。然而，在传染病中，一个人是否被感染往往取决于其他个体的感染结果，这种情况被称为相关事件。在此，我们以传染病为例回顾鲁宾提出的模型。违反稳定性假设的后果包括需要对结果进行扩展表示，以及存在不同类型的效应，如直接效应和间接效应。感兴趣的效应包括易感性的变化以及传染性的变化。我们通过以接触感染为条件，将传播概率正式定义为人群中效应的平均因果参数。使用这种因果推断模型很难正式定义无条件间接效应和总效应。当分配机制决定谁接触感染时，它会影响抽样机制，从而引发需要进一步探究的问题。我们通过对比不同程度接触感染在直接效应和间接效应中的作用来得出结论。
