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Causal Inference on Outcomes Learned from Text

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  • Iman Modarressi
  • Jann Spiess
  • Amar Venugopal

Abstract

We propose a machine-learning tool that yields causal inference on text in randomized trials. Based on a simple econometric framework in which text may capture outcomes of interest, our procedure addresses three questions: First, is the text affected by the treatment? Second, which outcomes is the effect on? And third, how complete is our description of causal effects? To answer all three questions, our approach uses large language models (LLMs) that suggest systematic differences across two groups of text documents and then provides valid inference based on costly validation. Specifically, we highlight the need for sample splitting to allow for statistical validation of LLM outputs, as well as the need for human labeling to validate substantive claims about how documents differ across groups. We illustrate the tool in a proof-of-concept application using abstracts of academic manuscripts.

Suggested Citation

  • Iman Modarressi & Jann Spiess & Amar Venugopal, 2025. "Causal Inference on Outcomes Learned from Text," Papers 2503.00725, arXiv.org.
  • Handle: RePEc:arx:papers:2503.00725
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    File URL: http://arxiv.org/pdf/2503.00725
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    References listed on IDEAS

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    1. Ashesh Rambachan & Rahul Singh & Davide Viviano, 2024. "Program Evaluation with Remotely Sensed Outcomes," Papers 2411.10959, arXiv.org, revised Apr 2025.
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