A growing number of scholars and data scientists are conducting randomized experiments to analyze causal relationships in network settings where units influence one another. A dominant methodology for analyzing these network experiments has been design-based, leveraging randomization of treatment assignment as the basis for inference. In this paper, we generalize this design-based approach so that it can be applied to more complex experiments with a variety of causal estimands with different target populations. An important special case of such generalized network experiments is a bipartite network experiment, in which the treatment assignment is randomized among one set of units and the outcome is measured for a separate set of units. We propose a broad class of causal estimands based on stochastic intervention for generalized network experiments. Using a design-based approach, we show how to estimate the proposed causal quantities without bias, and develop conservative variance estimators. We apply our methodology to a randomized experiment in education where a group of selected students in middle schools are eligible for the anti-conflict promotion program, and the program participation is randomized within this group. In particular, our analysis estimates the causal effects of treating each student or his/her close friends, for different target populations in the network. We find that while the treatment improves the overall awareness against conflict among students, it does not significantly reduce the total number of conflicts.

翻译：越来越多学者和数据科学家开展随机实验，以分析网络环境中个体间相互影响下的因果关系。分析这类网络实验的主流方法是基于设计的，即以处理分配的随机化作为推断基础。本文对该基于设计的方法进行推广，使其适用于包含多种因果估计量及不同目标总体的更复杂实验。这类广义网络实验的一个重要特例是二分网络实验，其中处理分配在一组单元中随机化，而结果变量则在另一组单元上测量。我们针对广义网络实验提出一类基于随机干预的广泛因果估计量。采用基于设计的方法，我们展示了如何无偏估计所提出的因果量，并发展了保守的方差估计量。我们将该方法应用于教育领域的随机实验：在该实验中，中学中一组被选中的学生有资格参与反冲突促进项目，且项目参与在该组内随机分配。具体而言，我们的分析估计了针对网络中不同目标总体，处理每位学生或其亲密好友的因果效应。研究发现，该处理虽提升了学生整体反对冲突的意识，但并未显著减少冲突总数。