English

Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts

Computation and Language 2022-02-03 v1 Machine Learning Applications Machine Learning

Abstract

We examine the role of textual data as study units when conducting causal inference by drawing parallels between human subjects and organized texts. %in human population research. We elaborate on key causal concepts and principles, and expose some ambiguity and sometimes fallacies. To facilitate better framing a causal query, we discuss two strategies: (i) shifting from immutable traits to perceptions of them, and (ii) shifting from some abstract concept/property to its constituent parts, i.e., adopting a constructivist perspective of an abstract concept. We hope this article would raise the awareness of the importance of articulating and clarifying fundamental concepts before delving into developing methodologies when drawing causal inference using textual data.

Keywords

Cite

@article{arxiv.2202.00848,
  title  = {Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts},
  author = {Bo Zhang and Jiayao Zhang},
  journal= {arXiv preprint arXiv:2202.00848},
  year   = {2022}
}

Comments

Accepted to CLeaR 2022

R2 v1 2026-06-24T09:15:01.252Z