Design of egocentric network-based studies to estimate causal effects under interference
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AI 根据所提供的论文材料归类：摘要直接研究网络干扰下的因果效应，并区分个体效应、溢出效应及总体效应。
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干扰条件下估计因果效应的自我中心网络研究设计
- 研究问题
- 当随机分配给部分个体的干预会影响其网络成员时，如何设计研究，分别估计直接受治者、未受治成员和整体人群的因果效应？
- 方法与思路
- 作者在潜在结果框架下研究自我中心网络随机设计，提出两种聚类回归建模方法，并推导检测个体、溢出及总体效应的样本量公式。
- 材料陈述的贡献
- 摘要称，方法明确了因果识别与估计所需假设，并分析组内相关系数、处理分配概率及每个网络成员数量对样本量需求的影响。
- 阅读关注点
- 应核对网络间及网络内干扰的允许范围、三类效应的具体定义、两种回归方法的识别条件，以及样本量公式如何处理网络相关性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列出具体识别假设、网络重叠处理方式及样本量公式的适用条件。
原文摘要
Many public health interventions are conducted in settings where individuals are connected to one another and the intervention assigned to randomly selected individuals may spill over to other individuals they are connected to. In these spillover settings, the effects of such interventions can be quantified in several ways. The average individual effect measures the intervention effect among those directly treated, while the spillover effect measures the effect among those connected to those directly treated. In addition, the overall effect measures the average intervention effect across the study population, over those directly treated along with those to whom the intervention spills over but who are not directly treated. Here, we develop methods for study design with the aim of estimating individual, spillover, and overall effects. In particular, we consider an egocentric network-based randomized design in which a set of index participants is recruited from the population and randomly assigned to treatment, while data are also collected from their untreated network members. We use the potential outcomes framework to define two clustered regression modeling approaches and clarify the underlying assumptions required to identify and estimate causal effects. We then develop sample size formulas for detecting individual, spillover, and overall effects. We investigate the roles of the intra-class correlation coefficient and the probability of treatment allocation on the required number of egocentric networks with a fixed number of network members for each egocentric network and vice-versa.