Randomized controlled trials are not only the golden standard in medicine and vaccine trials but have spread to many other disciplines like behavioral economics, making it an important interdisciplinary tool for scientists. When designing randomized controlled trials, how to assign participants to treatments becomes a key issue. In particular in the presence of covariate factors, the assignment can significantly influence statistical properties and thereby the quality of the trial. Another key issue is the widely popular assumption among experimenters that participants do not influence each other -- which is far from reality in a field study and can, if unaccounted for, deteriorate the quality of the trial. We address both issues in our work. After introducing randomized controlled trials bridging terms from different disciplines, we first address the issue of participant-treatment assignment in the presence of known covariate factors. Thereby, we review a recent assignment algorithm that achieves good worst-case variance bounds. Second, we address social spillover effects. Therefore, we build a comprehensive graph-based model of influence between participants, for which we design our own average treatment effect estimator $\hat \tau_{net}$. We discuss its bias and variance and reduce the problem of variance minimization to a certain instance of minimizing the norm of a matrix-vector product, which has been considered in literature before. Further, we discuss the role of disconnected components in the model's underlying graph.
翻译:由随机控制的试验不仅在医学和疫苗试验中具有黄金标准,而且已经扩散到行为经济学等许多其他学科,使之成为科学家的一个重要跨学科工具。在设计随机控制的试验时,如何指派参与者接受治疗就成为一个关键问题。特别是在出现共变因素的情况下,这种分配可以极大地影响统计特性,从而影响试验的质量。另一个关键问题是实验者中间广泛流行的假设,即参与者相互之间互不影响 -- -- 这在实地研究中远非现实,如果在实地研究中不为人知,则会降低试验的质量。我们在工作中处理这两个问题。在采用来自不同学科的随机控制的试验过渡术语后,我们首先在已知的共变数因素面前讨论参与者治疗分配问题。因此,我们审查最近的分配算法,得出了最坏情况差异的界限。第二,我们处理社会外溢效应。因此,我们建立了一个参与者之间影响的全面图表模型模型模型,我们为此设计了我们的平均治疗效果估计值 $\ hat\taunet}。我们先讨论其偏差和差异,然后在已知的共变因素之前,我们进一步讨论其基本标准讨论一个最小化的模型问题。