How to make causal inferences using texts.

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.

SUBMITTER: Egami N 

PROVIDER: S-EPMC9581481 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature