We study experimentation under endogenous network interference. Interference patterns are mediated by an endogenous graph, where edges can be formed or eliminated as a result of treatment. We show that conventional estimators are biased in these circumstances, and present a class of unbiased, consistent and asymptotically normal estimators of total treatment effects in the presence of such interference. We show via simulation that our estimator outperforms existing estimators in the literature. Our results apply both to bipartite experimentation, in which the units of analysis and measurement differ, and the standard network experimentation case, in which they are the same.

翻译：我们研究了内生网络干扰下的实验设计。干扰模式由内生图介导，其中边可以因处理而形成或消除。我们证明传统估计量在此类情况下存在偏差，并提出了一类在存在此类干扰时能够无偏、一致且渐近正态地估计总处理效应的估计量。通过仿真实验，我们证明该估计量优于文献中现有的估计方法。我们的研究结果同时适用于分析单元与测量单元相异的二分实验场景，以及二者相同的标准网络实验场景。