Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinc-tion is beginning to fade, with an emerging area of interdisciplinary research at the con-vergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportuni-ties in the application of causal inference to the textual domain, with its unique proper-ties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the ro-bustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.

Original languageEnglish (US)
Pages (from-to)1138-1158
Number of pages21
JournalTransactions of the Association for Computational Linguistics
Volume10
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Communication
  • Human-Computer Interaction
  • Linguistics and Language
  • Computer Science Applications
  • Artificial Intelligence

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