  While rankings are at the heart of social science research, little is known
about how to analyze ranking data in experimental studies. This paper
introduces a potential-outcomes framework to perform causal inference when
outcome data are ranking data. It introduces a class of causal estimands
tailored to ranked outcomes and develops methods for estimation and inference.
Furthermore, it extends the framework to partially ranked data. I show that
partial rankings can be considered a selection problem and propose
nonparametric sharp bounds for the treatment effects. Using the methods, I
reanalyze the recent study on blame attribution in the Stephon Clark shooting,
finding that people's attitudes toward officer-involved shootings are robust to
contextual information. I also apply the framework to study ballot order
effects in three ranked-choice voting (RCV) elections in 2022, proposing a new
theory of pattern rankings in RCV. Finally, I present three applications in
international relations.
