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

TL;DR

This paper explores how textual data can be used for causal inference by drawing parallels with human subjects, emphasizing the importance of clarifying fundamental concepts and proposing strategies for better causal framing.

Contribution

It introduces conceptual strategies for framing causal questions in textual data analysis, highlighting the need for clearer definitions and constructivist perspectives.

Findings

Highlighting ambiguities in causal concepts within textual data

Proposing two strategies for better causal framing in textual analysis

Emphasizing the importance of clarifying fundamental concepts before methodology development

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.