Replication Data for: Assessing Threats to Inference with Simultaneous Sensitivity Analysis: The Case of U.S. Supreme Court Oral Arguments

Political scientists relying on observational data face substantial challenges in drawing causal inferences. A particularly problematic threat to inference is the unobserved confounder. As a means to assess this threat, we introduce simultaneous sensitivity analysis to the political science literature. As an application, we consider the potentially confounded relationship between Supreme Court justice voting and oral argument quality. We demonstrate that this relationship is sensitive to the presence of a confounder, to a degree that threatens inference, and explore the confounder both theoretically and empirically. More generally, we show how sensitivity analysis can guide inquiry related to a covariate that cannot be directly measured.

依赖观测数据开展研究的政治学者，在进行因果推断时会面临诸多严峻挑战。其中，未观测混淆变量（unobserved confounder）是对推断过程极具破坏性的威胁因素。作为评估该威胁的有效手段，我们将同步敏感性分析（simultaneous sensitivity analysis）引入政治学研究领域。作为应用案例，我们考察了最高法院大法官投票与口头辩论质量之间可能受混淆变量干扰的关联。我们证明，该关联对混淆变量的存在具有显著敏感性，其敏感程度足以危及因果推断的有效性，并从理论与实证两个维度对该混淆变量展开探究。更具普遍意义的是，我们阐释了敏感性分析可如何为无法直接观测的协变量相关研究提供方法论指导。