Challenges of using text classifiers for causal inference

Zach Wood-Doughty; Ilya Shpitser; Mark Dredze

Challenges of using text classifiers for causal inference

Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

55 Scopus citations

Abstract

Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.

Original language	English (US)
Title of host publication	Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Editors	Ellen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
Publisher	Association for Computational Linguistics
Pages	4586-4598
Number of pages	13
ISBN (Electronic)	9781948087841
State	Published - 2018
Event	2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium Duration: Oct 31 2018 → Nov 4 2018

Publication series

Name	Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

Conference

Conference	2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Country/Territory	Belgium
City	Brussels
Period	10/31/18 → 11/4/18

Funding

This work was in part supported by the National Institute of General Medical Sciences under grant number 5R01GM114771 and by the National Institute of Allergy and Infectious Diseases under grant number R01 AI127271-01A1. We thank the anonymous reviewers for their helpful comments.

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

Wood-Doughty, Z., Shpitser, I., & Dredze, M. (2018). Challenges of using text classifiers for causal inference. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 4586-4598). (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018). Association for Computational Linguistics.

Wood-Doughty, Zach ; Shpitser, Ilya ; Dredze, Mark. / Challenges of using text classifiers for causal inference. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. editor / Ellen Riloff ; David Chiang ; Julia Hockenmaier ; Jun'ichi Tsujii. Association for Computational Linguistics, 2018. pp. 4586-4598 (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

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abstract = "Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.",

author = "Zach Wood-Doughty and Ilya Shpitser and Mark Dredze",

note = "Funding Information: This work was in part supported by the National Institute of General Medical Sciences under grant number 5R01GM114771 and by the National Institute of Allergy and Infectious Diseases under grant number R01 AI127271-01A1. We thank the anonymous reviewers for their helpful comments. Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 ; Conference date: 31-10-2018 Through 04-11-2018",

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series = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",

publisher = "Association for Computational Linguistics",

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Wood-Doughty, Z, Shpitser, I & Dredze, M 2018, Challenges of using text classifiers for causal inference. in E Riloff, D Chiang, J Hockenmaier & J Tsujii (eds), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, Association for Computational Linguistics, pp. 4586-4598, 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, Brussels, Belgium, 10/31/18.

Challenges of using text classifiers for causal inference. / Wood-Doughty, Zach; Shpitser, Ilya; Dredze, Mark.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. ed. / Ellen Riloff; David Chiang; Julia Hockenmaier; Jun'ichi Tsujii. Association for Computational Linguistics, 2018. p. 4586-4598 (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was in part supported by the National Institute of General Medical Sciences under grant number 5R01GM114771 and by the National Institute of Allergy and Infectious Diseases under grant number R01 AI127271-01A1. We thank the anonymous reviewers for their helpful comments. Publisher Copyright: © 2018 Association for Computational Linguistics

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AB - Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.

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Wood-Doughty Z, Shpitser I, Dredze M. Challenges of using text classifiers for causal inference. In Riloff E, Chiang D, Hockenmaier J, Tsujii J, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018. Association for Computational Linguistics. 2018. p. 4586-4598. (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018).

Challenges of using text classifiers for causal inference

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