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) |
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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 |
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Conference
Conference | 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 |
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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