How to make causal inferences using texts

Naoki Egami · Christian Fong · Justin Grimmer · Margaret E. Roberts · Brandon Stewart

118 citations0/0 files storedScience Advances

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Computational and Text Analysis Methods Sentiment Analysis and Opinion Mining Advanced Causal Inference Techniques

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.

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2011False-Positive Psychology 2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts 2002Psychological Aspects of Natural Language Use: Our Words, Our Selves 2014Structural Topic Models for Open‐Ended Survey Responses 2019stm : An R Package for Structural Topic Models 2003Extracting Policy Positions from Political Texts Using Words as Data 2019Uniting the Tribes: Using Text for Marketing Insight 2009How to Analyze Political Attention with Minimal Assumptions and Costs

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