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

Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects