A growing number of researchers are conducting randomized experiments to analyze causal relationships in network settings where units influence one another. A dominant methodology for analyzing these experiments is design-based, leveraging random treatment assignments as the basis for inference. In this paper, we generalize this design-based approach to accommodate complex experiments with a variety of causal estimands and different target populations. An important special case of such generalized network experiments is a bipartite network experiment, in which treatment is randomized among one set of units, and outcomes are measured on a separate set of units. We propose a broad class of causal estimands based on stochastic interventions for generalized network experiments. Using a design-based approach, we show how to estimate these causal quantities without bias and develop conservative variance estimators. We apply our methodology to a randomized experiment in education where participation in an anti-conflict promotion program is randomized among selected students. Our analysis estimates the causal effects of treating each student or their friends among different target populations in the network. We find that the program improves the overall conflict awareness among students but does not significantly reduce the total number of such conflicts.

翻译：越来越多的研究者正在通过随机实验来分析网络环境中单元相互影响下的因果关系。分析此类实验的主流方法是基于设计的，利用随机处理分配作为推断基础。本文将该基于设计的方法推广至具有多种因果估计量和不同目标群体的复杂实验。此类广义网络实验的一个重要特例是二分网络实验，其中处理在一组单元中随机分配，而在另一组独立单元中测量结果。我们提出了一类基于随机干预的广义网络实验因果估计量。采用基于设计的方法，我们展示了如何无偏地估计这些因果量，并建立了保守的方差估计量。我们将该方法应用于一项教育随机实验，该实验在选定学生中随机分配参与反冲突促进项目。我们的分析估计了在网络中不同目标群体内，处理每个学生或其朋友所产生的因果效应。研究发现，该项目提升了学生整体的冲突意识，但并未显著减少此类冲突的总数。