How to make causal inferences using texts

Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories with large collections of text. Nearly all text-based causal inferences depend on a latent representation of the text, but we show that estimating this latent representation from the data creates underacknowledged risks: we may introduce an identification problem or overfit. To address these risks, we introduce a split-sample workflow for making rigorous causal inferences with discovered measures as treatments or outcomes. We then apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.

Original languageEnglish (US)
Article numbereabg2652
JournalScience Advances
Volume8
Issue number42
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • General

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