Using observed language to understand interpersonal interactions is important in high-stakes decision making. We propose a causal research design for observational (non-experimental) data to estimate the natural direct and indirect effects of social group signals (e.g. race or gender) on speakers' responses with separate aspects of language as causal mediators. We illustrate the promises and challenges of this framework via a theoretical case study of the effect of an advocate's gender on interruptions from justices during U.S. Supreme Court oral arguments. We also discuss challenges conceptualizing and operationalizing causal variables such as gender and language that comprise of many components, and we articulate technical open challenges such as temporal dependence between language mediators in conversational settings.

通过观察语言以了解人际互动在高风险决策中的重要性。我们提出了一种因果研究设计，用于评估社会组信号（例如种族或性别）对发言人回应的自然直接和间接影响，并将语言的不同方面作为因果中介变量。通过理论案例研究（U.S.最高法院口述辩论中，倡导者性别对法官打断的影响），我们展示了这个框架的优势和挑战。同时，我们还讨论了概念化和操作因果变量（如性别和语言）时的挑战以及技术上的开放挑战，例如谈话设置中的语言中介变量之间的时间依赖关系。

利用文本作为因果中介变量：通过语言方面的因素对社会群体差异待遇进行因果估计的研究设计