Design and Analysis of Randomized Trials to Estimate Spatio-Temporally Heterogeneous Treatment Effects
估计时空异质处理效应的随机试验设计与分析AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要明确讨论随机试验中的因果效应识别,并将空间溢出作为设计与推断的关键问题。
估计时空异质处理效应的随机试验设计与分析AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
In this article, we consider randomized trial design to evaluate interventions with spatially or spatio-temporally heterogeneous effects. A common approach in this setting is the cluster randomized trial. In many cases, clusters are constituted as discrete subdivisions of a contiguous area of interest. However, cluster trials designed in this way may suffer from issues of spillover and may fail to capture the relevant spatial and temporal effects. We define possible randomization schemes and consider the identifiability of the causal effects of the intervention and the required trial design assumptions. We develop mixed model and dose response function specifications that satisfy the required assumptions. We show that the dose response functions are identifiable under certain randomization schemes and compare the performance of estimators in a simulation-based study. We demonstrate that obtaining valid inference may be difficult in these settings but that design-based estimators of confidence intervals and p-values generally perform well. Finally, we present design examples, including a re-analysis of data from a trial of an interventions to reduce the risk of malaria. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.