Exact p-Values for Network Interference

• 成果类型: Article
• 署名作者: Athey, Susan; Eckles, Dean; Imbens, Guido W.
• 署名单位: Stanford University; National Bureau of Economic Research; Massachusetts Institute of Technology (MIT)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1241178
• 发表日期: 2018
• 页码: 230-240
• 关键词: Causal Inference; identification; tests; units

摘要

We study the calculation of exact p-values for a large class of nonsharp null hypotheses about treatment effects in a setting with data from experiments involving members of a single connected network. The class includes null hypotheses that limit the effect of one unit's treatment status on another according to the distance between units, for example, the hypothesis might specify that the treatment status of immediate neighbors has no effect, or that units more than two edges away have no effect. We also consider hypotheses concerning the validity of sparsification of a network (e.g., based on the strength of ties) and hypotheses restricting heterogeneity in peer effects (so that, e.g., only the number or fraction treated among neighboring units matters). Our general approach is to define an artificial experiment, such that the null hypothesis that was not sharp for the original experiment is sharp for the artificial experiment, and such that the randomization analysis for the artificial experiment is validated by the design of the original experiment.