论文标题
与排名数据的因果推断：申请在警察暴力和投票命令中归因于排名选择的投票
Causal Inference with Ranking Data: Application to Blame Attribution in Police Violence and Ballot Order Effects in Ranked-Choice Voting
论文作者
论文摘要
尽管排名是社会科学研究的核心，但对于如何在实验研究中分析排名数据知之甚少。本文介绍了一个潜在的排出框架，以执行结果数据对数据进行排名。它引入了一类针对排名结果的因果估计，并开发了估计和推理的方法。此外，它将框架扩展到部分排名的数据。我表明，部分排名可以被视为选择问题，并提出了用于治疗效果的非参数尖锐界限。使用这些方法，我重新分析了斯蒂芬·克拉克（Stephon Clark）枪击中责任归因的最新研究，发现人们对涉及军官枪击事件的态度对上下文信息是有力的。我还将该框架应用于2022年的三项排名选举（RCV）选举中的投票顺序效应，并提出了RCV中新的模式排名理论。最后，我介绍了国际关系中的三个申请。
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.
