Estimation of Monotone Treatment Effects in Network Experiments

• 成果类型: Article
• 署名作者: Choi, David
• 署名单位: Carnegie Mellon University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1194845
• 发表日期: 2017
• 页码: 1147-1155
• 关键词: Causal Inference; randomized experiments; interference; units; education; contagion

摘要

Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for finding confidence intervals on the attributable treatment effect in such settings. The methods do not require partial interference, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects. Network or spatial information can be used to customize the test statistic; in principle, this can increase power without making assumptions on the data-generating process. Supplementary materials for this article are available online.