@inproceedings{tierney-volfovsky-2021-sensitivity,
title = "Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization",
author = "Tierney, Graham and
Volfovsky, Alexander",
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-1.5",
doi = "10.18653/v1/2021.cinlp-1.5",
pages = "61--73",
abstract = "We introduce a procedure to examine a text-as-mediator problem from a novel randomized experiment that studied the effect of conversations on political polarization. In this randomized experiment, Americans from the Democratic and Republican parties were either randomly paired with one-another to have an anonymous conversation about politics or alternatively not assigned to a conversation {---} change in political polarization over time was measured for all participants. This paper analyzes the text of the conversations to identify potential mediators of depolarization and is faced with a unique challenge, necessitated by the primary research hypothesis, that individuals in the control condition do not have conversations and so lack observed text data. We highlight the importance of using domain knowledge to perform dimension reduction on the text data, and describe a procedure to characterize indirect effects via text when the text is only observed in one arm of the experiment.",
}
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%0 Conference Proceedings
%T Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization
%A Tierney, Graham
%A Volfovsky, Alexander
%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 tierney-volfovsky-2021-sensitivity
%X We introduce a procedure to examine a text-as-mediator problem from a novel randomized experiment that studied the effect of conversations on political polarization. In this randomized experiment, Americans from the Democratic and Republican parties were either randomly paired with one-another to have an anonymous conversation about politics or alternatively not assigned to a conversation — change in political polarization over time was measured for all participants. This paper analyzes the text of the conversations to identify potential mediators of depolarization and is faced with a unique challenge, necessitated by the primary research hypothesis, that individuals in the control condition do not have conversations and so lack observed text data. We highlight the importance of using domain knowledge to perform dimension reduction on the text data, and describe a procedure to characterize indirect effects via text when the text is only observed in one arm of the experiment.
%R 10.18653/v1/2021.cinlp-1.5
%U https://aclanthology.org/2021.cinlp-1.5
%U https://doi.org/10.18653/v1/2021.cinlp-1.5
%P 61-73
Markdown (Informal)
[Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization](https://aclanthology.org/2021.cinlp-1.5) (Tierney & Volfovsky, CINLP 2021)
ACL