Randomized Controlled Trials Under Influence: Covariate Factors and Graph-Based Network Interference
影响下的随机对照试验：协变量因素与图网络干扰AI 解读 ↓
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影响下的随机对照试验：协变量因素与图网络干扰
- 研究问题
- 研究随机对照试验如何兼顾已知协变量下的处理分配和参与者之间的社会溢出，并控制效应估计的偏差与方差。
- 方法与思路
- 作者回顾具有最坏情形方差界的分配算法，构建参与者影响的图模型，并设计平均处理效应估计量及方差优化问题。
- 材料陈述的贡献
- 摘要称，分析了网络效应估计量的偏差和方差，将方差最小化归结为矩阵向量乘积范数优化，并讨论图中不连通分量。
- 阅读关注点
- 应核对网络影响如何进入潜在结果、目标效应的定义、处理分配机制，以及方差优化与不连通分量分析的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明网络是否已知、估计量无偏条件及优化方法的实际效果。
原文摘要
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 $\hat τ_{net}$. 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.