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While rankings are at the heart of social science research, little is knownabout how to analyze ranking data in experimental studies. This paperintroduces a potential-outcomes framework to perform causal inference whenoutcome data are ranking data. It introduces a class of causal estimandstailored to ranked outcomes and develops methods for estimation and inference.Furthermore, it extends the framework to partially ranked data. I show thatpartial rankings can be considered a selection problem and proposenonparametric sharp bounds for the treatment effects. Using the methods, Ireanalyze the recent study on blame attribution in the Stephon Clark shooting,finding that people's attitudes toward officer-involved shootings are robust tocontextual information. I also apply the framework to study ballot ordereffects in three ranked-choice voting (RCV) elections in 2022, proposing a newtheory of pattern rankings in RCV. Finally, I present three applications ininternational relations.

Causal Inference with Ranking Data: Application to Blame Attribution in Police Violence and Ballot Order Effects in Ranked-Choice Voting