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

Katherine Keith, Douglas Rice, Brendan O’Connor


Abstract
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.
Anthology ID:
2021.cinlp-1.2
Volume:
Proceedings of the First Workshop on Causal Inference and NLP
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
CINLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–32
Language:
URL:
https://aclanthology.org/2021.cinlp-1.2
DOI:
10.18653/v1/2021.cinlp-1.2
Bibkey:
Cite (ACL):
Katherine Keith, Douglas Rice, and Brendan O’Connor. 2021. Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects. In Proceedings of the First Workshop on Causal Inference and NLP, pages 21–32, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects (Keith et al., CINLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2021.cinlp-1.2.pdf