  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.
