How to Make Causal Inferences Using Texts
Abstract
New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences with discovered measures as a treatment or outcome. Our framework enables researchers to discover high-dimensional textual interventions and estimate the ways that observed treatments affect text-based outcomes. We argue that nearly all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. But estimating this latent representation, we show, creates new risks: we may introduce an identification problem or overfit. To address these risks we describe a split-sample framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic response. Our work provides a rigorous foundation for text-based causal inferences.
Cite
@article{arxiv.1802.02163,
title = {How to Make Causal Inferences Using Texts},
author = {Naoki Egami and Christian J. Fong and Justin Grimmer and Margaret E. Roberts and Brandon M. Stewart},
journal= {arXiv preprint arXiv:1802.02163},
year = {2018}
}
Comments
47 pages