@inproceedings{keith-etal-2021-text,
title = "Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects",
author = "Keith, Katherine and
Rice, Douglas and
O{'}Connor, Brendan",
editor = "Feder, Amir and
Keith, Katherine and
Manzoor, Emaad and
Pryzant, Reid and
Sridhar, Dhanya and
Wood-Doughty, Zach and
Eisenstein, Jacob and
Grimmer, Justin and
Reichart, Roi and
Roberts, Molly and
Shalit, Uri and
Stewart, Brandon and
Veitch, Victor and
Yang, Diyi",
booktitle = "Proceedings of the First Workshop on Causal Inference and NLP",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.cinlp-1.2",
doi = "10.18653/v1/2021.cinlp-1.2",
pages = "21--32",
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.",
}
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%0 Conference Proceedings
%T Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects
%A Keith, Katherine
%A Rice, Douglas
%A O’Connor, Brendan
%Y Feder, Amir
%Y Keith, Katherine
%Y Manzoor, Emaad
%Y Pryzant, Reid
%Y Sridhar, Dhanya
%Y Wood-Doughty, Zach
%Y Eisenstein, Jacob
%Y Grimmer, Justin
%Y Reichart, Roi
%Y Roberts, Molly
%Y Shalit, Uri
%Y Stewart, Brandon
%Y Veitch, Victor
%Y Yang, Diyi
%S Proceedings of the First Workshop on Causal Inference and NLP
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F keith-etal-2021-text
%X 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.
%R 10.18653/v1/2021.cinlp-1.2
%U https://aclanthology.org/2021.cinlp-1.2
%U https://doi.org/10.18653/v1/2021.cinlp-1.2
%P 21-32
Markdown (Informal)
[Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects](https://aclanthology.org/2021.cinlp-1.2) (Keith et al., CINLP 2021)
ACL